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For interpolating kernel machines, minimizing the norm of the ERM solution minimizes stability | 1 INTRODUCTION . Statistical learning theory studies the learning properties of machine learning algorithms , and more fundamentally , the conditions under which learning from finite data is possible . In this context , classical learning theory focuses on the size of the hypothesis space in terms of different complexi... | This paper investigates kernel ridge-less regression from a stability viewpoint by deriving its risk bounds. Using stability arguments to derive risk bounds have been widely adopting in machine learning. However, related studies on kernel ridge-less regression are still sparse. The present study fills this gap, which, ... | SP:4d08cdb2de2044bcb574a425b42963b83fbebfbc |
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning | 1 INTRODUCTION . In the real world , we are often faced with situations where data distributions are changing over time , and we would like to update our models by new data in time , with bounded growth in system size . These situations fall under the umbrella of “ continual learning ” , which has many practical applic... | This paper presents a novel way of making full use of compact episodic memory to alleviate catastrophic forgetting in continual learning. This is done by adding the proposed discriminative representation loss to regularize the gradients produced by new samples. Authors gave insightful analysis on the influence of gradi... | SP:b80bc890180934092cde037b49d94d6e4e06fad9 |
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks | 1 INTRODUCTION . The process of Multi-Task Learning ( MTL ) on a set of related tasks is inspired by the patterns displayed by human learning . It involves a pretraining phase over all the tasks , followed by a finetuning phase . During pretraining , the model tries to grasp the shared knowledge of all the tasks involv... | This paper proposes a new framework that computes the task-specific representations to modulate the model parameters during the multi-task learning (MTL). This framework uses a single model with shared representations for learning multiple tasks together. Also, explicit task information may not be always available, in ... | SP:09f2fe6a482bbd6f9bd2c62aa841f995171ba939 |
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach | 1 INTRODUCTION . Hybrid electric vehicles powered by fuel cells and batteries have attracted great enthusiasm in modern days as they have the potential to eliminate emissions from the transport sector . Now , both the fuel cells and batteries have got several operational challenges which make the separate use of each o... | This work proposes a deep reinforcement learning-based optimization strategy to the fuel optimization problem for the hybrid electric vehicle. The problem has been formulated as a fully observed stochastic Markov Decision Process (MDP). A deep neural network is used to parameterize the policy and value function. A cont... | SP:a1e2218e6943bf138aeb359e23628676b396ed66 |
Neural representation and generation for RNA secondary structures | 1 INTRODUCTION . There is an increasing interest in developing deep generative models for biochemical data , especially in the context of generating drug-like molecules . Learning generative models of biochemical molecules can facilitate the development and discovery of novel treatments for various diseases , reducing ... | This paper proposes 3 deep generative models based on VAEs (with different encoding schemes for RNA secondary structure) for the generation of RNA secondary structures. They test each model on 3 benchmark tasks: unsupervised generation, semi-supervised learning and targeted generation. This paper has many interesting ... | SP:43e525fb3fa611df7fd44bd3bc9843e57b154c66 |
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT | 1 INTRODUCTION AND RELATED WORK . The advances in neural machine translation ( NMT ) systems have led to great achievements in terms of state-of-the-art performance in automatic translation tasks . There have even been claims that their translations are no worse than what an average bilingual human may produce ( Wu et ... | This paper presents a benchmark for discourse phenomena in machine translation. Its main novelty lies in the relatively large scale, spanning three translation directions, four discourse phenomena, and 150-5000 data points per language and phenomenon. A relatively large number of systems from previous work is benchmark... | SP:0bd749fe44c37b521bd40f701e1428890aaa9c95 |
Private Image Reconstruction from System Side Channels Using Generative Models | 1 INTRODUCTION . Side channel analysis ( SCA ) recovers program secrets based on the victim program ’ s nonfunctional characteristics ( e.g. , its execution time ) that depend on the values of program secrets . SCA constitutes a major threat in today ’ s system and hardware security landscape . System side channels , s... | The authors present a framework that uses a combination of VAE and GAN to recover private user images using Side channel analysis of memory access . A VAE-LP model first reconstructs a coarse image from side channel information which is reshaped and processed using a convolutional network. The output of the VAE-LP mo... | SP:b2fc6ca65add04fb32bcf7622d9098de9004ca2b |
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation | Deep ensembles perform better than a single network thanks to the diversity among their members . Recent approaches regularize predictions to increase diversity ; however , they also drastically decrease individual members ’ performances . In this paper , we argue that learning strategies for deep ensembles need to tac... | This paper proposes a method of learning ensembles that adhere to an "ensemble version" of the information bottleneck principle. Whereas the information bottleneck principle says the representation should avoid spurious correlations between the representation (Z) and the training data (X) that is not useful for predict... | SP:7fb11c941e8d79248ce5ff7caa0535a466303395 |
Zero-shot Synthesis with Group-Supervised Learning | 1 INTRODUCTION . Primates perform well at generalization tasks . If presented with a single visual instance of an object , they often immediately can generalize and envision the object in different attributes , e.g. , in different 3D pose ( Logothetis et al. , 1995 ) . Primates can readily do so , as their previous kno... | The paper proposed a new training framework, namely GSL, for novel content synthesis. And GSL enables learning of disentangled representations of tangible attributes and achieve novel image synthesis by recombining those swappable components under a zero-shot setting. The framework leverages the underlying semantic lin... | SP:5561773ab024b083be4e362db079e371abf79653 |
Asymmetric self-play for automatic goal discovery in robotic manipulation | 1 INTRODUCTION . We are motivated to train a single goal-conditioned policy ( Kaelbling , 1993 ) that can solve any robotic manipulation task that a human may request in a given environment . In this work , we make progress towards this goal by solving a robotic manipulation problem in a table-top setting where the rob... | This paper presents an approach to learn goal conditioned policies by relying on self-play which sets the goals and discovers a curriculum of tasks for learning. Alice and Bob are the agents. Alice's task is to set a goal by following a number of steps in the environment and she is rewarded when the goal is too challen... | SP:9f70871f0111b58783f731748d8750c635998f32 |
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization | 1 Introduction . Graph neural networks ( GNNs ) have been intensively studied recently [ 29 , 26 , 39 , 68 ] , due to their established performance towards various real-world tasks [ 15 , 69 , 53 ] , as well as close connections to spectral graph theory [ 12 , 9 , 16 ] . While most GNN architectures are not very compli... | The paper introduces a theoretical framework for analyzing GNN transferability. The main idea is to view a graph as subgraph samples with the information of both the connections and the features. Based on this view, the authors define EGI score of a graph as a learnable function that needs to be optimized by maximizing... | SP:038a1d3066f8273977337262e975d7a7aab5002f |
Information Lattice Learning | 1 INTRODUCTION . With rapid progress in AI , there is an increasing desire for general AI ( Goertzel & Pennachin , 2007 ; Chollet , 2019 ) and explainable AI ( Adadi & Berrada , 2018 ; Molnar , 2019 ) , which exhibit broad , human-like cognitive capacities . One common pursuit is to move away from “ black boxes ” desig... | The authors perform a descriptive analysis of data by attempting to identify elements in the partial ordering of all partitions on the data which admit a compact definition. Compact definitions are those that are formed by composition of a small number of predefined (prior) set of mathematical operations. Projection an... | SP:40cba7b6c04d7e44709baed351382c27fa89a129 |
Don't be picky, all students in the right family can learn from good teachers | 1 INTRODUCTION . Recently-developed deep learning models have achieved remarkable performance in a variety of tasks . However , breakthroughs leading to state-of-the-art ( SOTA ) results often rely on very large models : GPipe , Big Transfer and GPT-3 use 556 million , 928 million and 175 billion parameters , respectiv... | This paper proposes searching for an architecture generator that outputs good student architectures for a given teacher. The authors claim that by learning the parameters of the generator instead of relying directly on the search space, it is possible to explore the search space of architectures more effectively, incre... | SP:1ee00313e354c4594bbf6cf8bdbe33e3ec8df62f |
Towards Counteracting Adversarial Perturbations to Resist Adversarial Examples | 1 INTRODUCTION . Deep neural networks ( DNNs ) have become the dominant approach for various tasks including image understanding , natural language processing and speech recognition ( He et al. , 2016 ; Devlin et al. , 2018 ; Park et al. , 2018 ) . However , recent studies demonstrate that neural networks are vulnerabl... | The paper proposes a defense that works by adding multiple targeted adversarial perturbations (with random classes) on the input sample before classifying it. There is little theoretical reasoning for why this is a sensible defense. More importantly though, the defense is only evaluated in an oblivious threat model whe... | SP:eea3b3ec32cce61d6b6df8574cf7ce9376f2230a |
Defuse: Debugging Classifiers Through Distilling Unrestricted Adversarial Examples | 1 INTRODUCTION . Debugging machine learning ( ML ) models is a critical part of the ML development life cycle . Uncovering bugs helps ML developers make important decisions about both development and deployment . In practice , much of debugging uses aggregate test statistics ( like those in leader board style challenge... | The technique is described in sufficient detail and the paper is easy to read. Experimental results involving three datasets: MNIST, street view house numbers, and German traffic signs. The experimental results show that the proposed technique finds significant failures in all datasets, including critical failure scena... | SP:8badc3f75194e9780063af5a2f26448e41e733d4 |
Improving Learning to Branch via Reinforcement Learning | 1 INTRODUCTION . Mixed Integer Programming ( MIP ) has been applied widely in many real-world problems , such as scheduling ( Barnhart et al. , 2003 ) and transportation ( Melo & Wolsey , 2012 ) . Branch and Bound ( B & B ) is a general and widely used paradigm for solving MIP problems ( Wolsey & Nemhauser , 1999 ) . B... | The paper proposes a model for *variable selection* in *Mixed Integer Programming (MIP)* solvers. While this problem is clearly a sequential decision making task, modeling it as an MDP is challenging. As a result, existing works use other approaches such as ranking or imitation learning. This paper overcomes these chal... | SP:bbaedd5d8e7591fa3a5587260bf19f3d05779976 |
Frequency Decomposition in Neural Processes | Neural Processes are a powerful tool for learning representations of function spaces purely from examples , in a way that allows them to perform predictions at test time conditioned on so-called context observations . The learned representations are finite-dimensional , while function spaces are infinite-dimensional , ... | The work examines properties of Neural Processes (NP). More precisely, of deterministic NPs and how they for finite-dimensional representations of infinite-dimensional function spaces. NP learn functions f that best represent/fit discrete sets of points in space. Based on signal theoretic aspects of discretisation, aut... | SP:a20769de2c7acf390c7e3bece904a17df6a991bd |
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have shown amazing performance on many singleagent ( SA ) environment tasks ( Mnih et al. , 2013 ) ( Jaderberg et al. , 2016 ) ( Oh et al. , 2018 ) . However , for many real-world problems , the environment is much more complex where RL agents often need to coop... | The paper deals with the problem of credit assignment and synchronous estimation in cooperative multi-agent reinforcement learning problems. The authors introduce marginal advantage functions and use them for the estimation of the counterfactual advantage function. These functions permit to decompose the Multi-Agent Po... | SP:ba25b5b02701e01998e9dd22e4230c4e095f4542 |
Adaptive Stacked Graph Filter | We study Graph Convolutional Networks ( GCN ) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fullyconnected weights versus trainable polynomial coefficients . We find that by stacking graph filters with learnable polynomial parameters , we can build a highly ad... | This paper addresses the problem of vertex classification using a new Graph Convolutional Neural Network (NN) architecture. The linear operator within each of the layers of the GNNN is formed by a polynomial graph filter (i.e., a matrix polynomial of either the adjacency or the Laplacian novelty). Rather than working o... | SP:37bdb147b866b9e32a94d55dae82d7a42cea8da9 |
Deep $k$-NN Label Smoothing Improves Reproducibility of Neural Network Predictions | 1 INTRODUCTION . Deep neural networks ( DNNs ) have proved to be immensely successful at solving complex classification tasks across a range of problems . Much of the effort has been spent towards improving their predictive performance ( i.e . accuracy ) , while comparatively little has been done towards improving the ... | The main objective of this paper is to reduce the model stability, in particular, the prediction churn of neural networks. The prediction churn is defined as the changed prediction w.r.t. model randomness, e.g. multiple runs of networks. The paper proposed to use a interpolated version of global label smoothing and k-N... | SP:f19be0fdce321827638f91d57607ba340b1c3e4b |
Adversarial Feature Desensitization | 1 Introduction . When training a classifier , it is common to assume that the training and test samples are drawn from the same underlying distribution . In adversarial machine learning , however , this assumption is intentionally violated by using the classifier itself to perturb the samples from the original ( natura... | This paper proposes Adversarial Feature Desensitization (AFD) as a defense against adversarial examples. AFD employs a min-max adversarial learning framework where the classifier learns to encode features of both clean and adversarial images as the same distribution, thereby desensitizing adversarial features. With the... | SP:5751b2abad772e44e69e125a769f25892c2a2e30 |
Syntactic representations in the human brain: beyond effort-based metrics | 1 INTRODUCTION . Neuroscientists have long been interested in how the brain processes syntax . To date , there is no consensus on which brain regions are involved in processing it . Classically , only a small number of regions in the left hemisphere were thought to be involved in language processing . More recently , t... | This paper derives various types of graph embeddings to encode aspects of syntactic information that the brain may be processing during real-time sentence comprehension. These embeddings, along with indicators of punctuation, POS and dependency tags, and BERT embeddings, are used to predict brain activity recorded via ... | SP:95ba9ad102adafaabf9671737e6549728d104629 |
Analogical Reasoning for Visually Grounded Compositional Generalization | Children acquire language subconsciously by observing the surrounding world and listening to descriptions . They can discover the meaning of words even without explicit language knowledge , and generalize to novel compositions effortlessly . In this paper , we bring this ability to AI , by studying the task of multimod... | This paper explores the problem of generalizing to novel combinations of verbs and nouns in a task for captioning video stills from videos about cooking. The paper introduces a new dataset based off of EPIC-Kitchens (Damen et al. 2018) which masks out verbs and nouns and splits the evaluation data into seen combination... | SP:7327dc440b5c193c1dda156276860f89594721fa |
A Unified Framework for Convolution-based Graph Neural Networks | 1 INTRODUCTION . Recent years have witnessed a fast development in graph processing by generalizing convolution operation to graph-structured data , which is known as Graph Convolutional Networks ( GCNs ) ( Kipf & Welling , 2017 ) . Due to the great success , numerous variants of GCNs have been developed and extensivel... | This paper presents a unified framework for graph convolutional neural networks based on regularized optimization, connecting different variants of graph neural networks including vanilla, attention-based, and topology-based approaches. The authors also propose a novel regularization technique to approach the oversmo... | SP:5be9a3c39234c10c226c42eec95e29cbddbaf8c0 |
Benchmarks for Deep Off-Policy Evaluation | 1 INTRODUCTION . Reinforcement learning algorithms can acquire effective policies for a wide range of problems through active online interaction , such as in robotics ( Kober et al. , 2013 ) , board games and video games ( Tesauro , 1995 ; Mnih et al. , 2013 ; Vinyals et al. , 2019 ) , and recommender systems ( Aggarwa... | This article proposes a benchmark of off-policy evaluation, which provides different metrics for policy ranking, evaluation and selection. Offline metrics are provided by evaluating the value function of logged data, and then evaluating absolute error, rank correlation and regret. Verify the effectiveness of different ... | SP:dd2a50abff85d2b52b02dfe27cd42e443ea265cf |
Triple-Search: Differentiable Joint-Search of Networks, Precision, and Accelerators | 1 INTRODUCTION . The powerful performance and prohibitive complexity of deep neural networks ( DNNs ) have fueled a tremendous demand for efficient DNN accelerators which could boost DNN acceleration efficiency by orders-of-magnitude ( Chen et al. , 2016 ) . In response , extensive research efforts have been devoted to... | This paper proposes Triple-Search (TRIPS), a differentiable framework of jointly searching for network architecture, quantization precision, and accelerator parameters. To address the dilemma between exploding training memory and biased search, the proposed framework leverages heterogeneous sampling where soft Gumbel S... | SP:1037f94ce6eae4a42ea7913c76007f5f3c26aeaf |
Gradient Based Memory Editing for Task-Free Continual Learning | 1 INTRODUCTION . Accumulating past knowledge and adapting to evolving environments are one of the key traits in human intelligence ( McClelland et al. , 1995 ) . While contemporary deep neural networks have achieved impressive results in a range of machine learning tasks Goodfellow et al . ( 2015 ) , they haven ’ t yet... | This paper deals with continual learning. Specifically, given a stream of tasks we want to maximise performance across all tasks. Typically neural networks suffer from catastrophic forgetting which results in worse performance on tasks seen earlier in training. There are many proposed solutions to this problem. One spe... | SP:d850572819200f79545616fc92e789ce958b30d4 |
Improving Transformation Invariance in Contrastive Representation Learning | 1 INTRODUCTION . Learning meaningful representations of data is a central endeavour in artificial intelligence . Such representations should retain important information about the original input whilst using fewer bits to store it ( van der Maaten et al. , 2009 ; Gregor et al. , 2016 ) . Semantically meaningful represe... | Given one image, the paper first generates different views which are controlled by differentiable parameter \alpha, and then minimizes the additional "conditional variance" term~(expectation of these views' squared differences). Therefore, the paper encourages representations of the same image remain similar under the ... | SP:a692e1e43991839e08a02e9122757224e1582cfd |
Understanding the Effect of Bias in Deep Anomaly Detection | 1 INTRODUCTION . Anomaly detection ( Chandola et al. , 2009 ; Pimentel et al. , 2014 ) trains a formal model to identify unexpected or anomalous instances in incoming data , whose behaviors differ from normal instances . It is particularly useful for detecting problematic events such as digital fraud , structural defec... | This paper studies the potential bias in deep semi-supervised anomaly detection. The bias is evaluated in terms of TPR rate given a fixed FPR rate. It uses the anomaly scores output by unsupervised anomaly detectors as a benchmark to examine the relative scoring bias in deep semi-supervised anomaly detectors. It furthe... | SP:a24603a5dbc07070aeba98e1206511799111bec6 |
Calibration tests beyond classification | 1 INTRODUCTION . We consider the general problem of modelling the relationship between a featureX and a target Y in a probabilistic setting , i.e. , we focus on models that approximate the conditional probability distribution P ( Y |X ) of target Y for given feature X . The use of probabilistic models that output a pro... | The authors present an approach for testing calibration in conditional probability estimation models. They build on a line of work in the kernel estimation literature assessing whether the conditional distributions are well calibrated (i.e. P(Y | f(X)) = f(X), where f is some predictive model). They develop an MMD kern... | SP:cf6c9061542bf9c43a968faa574ce03ad71a859a |
Semantic Hashing with Locality Sensitive Embeddings | 1 INTRODUCTION . One of most challenging aspects in many Information Retrieval ( IR ) systems is the discovery and identification of the nearest neighbors of a query element in an vector space . This is typically solved using Approximate Nearest Neighbors ( ANN ) methods as exact solutions typically do not scale well w... | The authors consider the problem of learning a hash function such that semantically similar elements have high collision probability. They modify the approach Deep Hashing Networks (Zhu et al., 2016) with a new loss function. Rather than use a sigmoid based loss function, the authors argue that a loss function based o... | SP:becb496310e88c1e2e7d03131093b9ebcf075c1d |
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness | 1 INTRODUCTION . When models are tested on distributions that are different from the training distribution , they typically suffer large drops in performance ( Blitzer and Pereira , 2007 ; Szegedy et al. , 2014 ; Jia and Liang , 2017 ; AlBadawy et al. , 2018 ; Hendrycks et al. , 2019a ) . For example , in remote sensin... | This paper introduces a new method for leveraging auxiliary information and unlabelled data to improve out-of-distribution model performance. Theoretically, in a linear model with latent variables, they demonstrate using auxiliary data as inputs helps in-distribution test-error, but can hurt out-of-distribution error, ... | SP:7611ee6b9dfabf7ec6a65da58cb6e3892705e1c9 |
Variance Reduction in Hierarchical Variational Autoencoders | 1 INTRODUCTION . Variational autoencoders ( VAE ) [ 10 ] are a popular latent variable model for unsupervised learning that simplifies learning by the introduction of a learned approximate posterior . Given data x and latent variables z , we specify the conditional distribution p ( x|z ) by parameterizing the distribut... | This paper studies the training of deep hierarchical VAEs and focuses on the problem of posterior collapse. It is argued that reducing the variance of the gradient estimate may help to overcome posterior collapse. The authors focus on reducing the variance of the functions parameterizing the variational distribution of... | SP:b6dd62914f7464efb601c6d9f8a4d35e047447d5 |
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation | 1 INTRODUCTION . Many real-world optimization problems involve function evaluations that are the result of expensive or time-consuming process . Examples occur in the design of materials ( Mansouri Tehrani et al. , 2018 ) , proteins ( Brookes et al. , 2019 ; Kumar & Levine , 2019 ) , neural network architectures ( Zoph... | The paper proposes an approximation method, called NEMO (Normalized maximum likelihood Estimation for model-based optimization) to compute the conditional normalized maximum log-likelihood of a query data point as a way to quantify the uncertainty in a forward prediction model in offline model-based optimization probl... | SP:2d25eeb93ba90f9c4064bf794f9a132a6859c8e4 |
Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs | 1 INTRODUCTION . Transformer-based models yield state-of-the-art results on a number of tasks , including representation learning ( Devlin et al. , 2019 ; Liu et al. , 2019 ; Clark et al. , 2020 ) and generation ( Radford et al . ; Raffel et al. , 2019 ; Lewis et al. , 2020 ) . Notably , large language models have been... | This paper proposes a simple approach to discover interpretable latent manipulations in trained text VAEs. The method essentially involves performing PCA on the latent representations to find directions that maximize variance. The authors argue that this results in more interpretable directions. The method is applied o... | SP:ce75f565c3c17363695c9e39f28b49a66e3731b8 |
Nonvacuous Loss Bounds with Fast Rates for Neural Networks via Conditional Information Measures | √ n dependence . We demonstrate the usefulness of our tail bounds by showing that they lead to estimates of the test loss achievable with several neural network architectures trained on MNIST and Fashion-MNIST that match the state-of-the-art bounds available in the literature . 1 INTRODUCTION . In recent years , there ... | This paper extends results of prior work by Steinke and Zakynthinou, by providing generalization bounds in the PAC-Bayesian and single-draw settings that depend on the conditional mutual information. The emphasis in this work is on obtaining fast rates ($1/n$ vs. $1/\sqrt{n}$). The authors also conduct empirical experi... | SP:b9d78677e836fddeab78615ad35e9545d9c1d08f |
Neural Time-Dependent Partial Differential Equation | 1 INTRODUCTION . The research of time-dependent partial differential equations ( PDEs ) is regarded as one of the most important disciplines in applied mathematics . PDEs appear ubiquitously in a broad spectrum of fields including physics , biology , chemistry , and finance , to name a few . Despite their fundamental i... | This work proposes a sequence-to-sequence approach for learning the time evolution of PDEs. The method employs a bi-directional LSTM to predict solutions of a PDE-based formulation for a chosen number of time steps. By itself this is an interesting, and important goal, but the method does not seem to contain any novel ... | SP:29a7b851d3edc2176467adc75ba67cc973a11a37 |
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning | 1 INTRODUCTION . The impressive performance exhibited by modern machine learning models hinges on the ability to train the aforementioned models on a very large amounts of labeled data . In practice , in many real world scenarios , even when raw data exists aplenty , acquiring labels might prove challenging and/or expe... | In this paper, the authors develop a data selection scheme aimed to minimize a notion of Bayes excess risk for overparametrized linear models. The excess Bayes risk is the expected squared error between the prediction and the target. The authors note that solutions such as V-optimality exist for the underparametrized c... | SP:797b07cd8142a35333037bb573db0dfe5dde65ac |
Offline Policy Optimization with Variance Regularization | 1 INTRODUCTION . Offline batch reinforcement learning ( RL ) algoithms are key towards scaling up RL for real world applications , such as robotics ( Levine et al. , 2016 ) and medical problems . This is because offline RL provides the appealing ability for agents to learn from fixed datasets , similar to supervised le... | This paper proposes a novel algorithm for offline policy optimization. The main idea is to prevent overestimation bias by regularizing against the variance of the importance weighted value estimate. There are two key modifications: (1) using an importance weight from the stationary distribution and (2) using Fenchel du... | SP:4989f7703e106a20401cec0a5058d440720b0379 |
Quantifying Statistical Significance of Neural Network Representation-Driven Hypotheses by Selective Inference | 1 INTRODUCTION . The remarkable predictive performance of deep neural networks ( DNNs ) stems from their ability to learn appropriate representations from data . In order to understand the decision-making process of DNNs , it is thus important to be able to explain and interpret DNN representations . For example , in i... | This paper proposed a novel method which to quantify the reliability of DNN-driven hypotheses in a statistical hypothesis testing framework. Naive statistical testings are not appropriate for the DNN-driven hypotheses, where the hypotheses are selected by looking at the data(i.e. The selection bias exists). To address ... | SP:4e77d43eb99688600f6c2115e1882e0b1e11a751 |
Gradient descent temporal difference-difference learning | 1 INTRODUCTION . Off-policy algorithms for value function learning enable an agent to use a behavior policy that differs from the target policy in order to gain experience for learning . However , because off-policy methods learn a value function for a target policy given data due to a different behavior policy , they ... | This paper proposes a variant of the GTD2 algorithm by adding an additional regularization term to the objective function, and the new algorithm is named as Gradient-DD (GDD). The regularization ensures that the value function does not change drastically between consecutive iterations. The authors show that the update ... | SP:8a32dfc80f31fd3da97e15ce98193144d03836b5 |
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning | We propose a simple class of deep reinforcement learning ( RL ) methods , called FactoredRL , that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms . In tabular and linear approximation settings , the factored Markov decision process lite... | This paper presents a methodology for incorporating factor-graphs into model-based and model-free RL methods. The work starts by assuming access to a correct and factor graph showing the relationship between individual state factors, actions, and rewards. The authors propose to make use of this factor graph by using a ... | SP:dcb62a0cc1b03e9ea24b2ed167f14255d9386f95 |
Parallel Training of Deep Networks with Local Updates | 1 INTRODUCTION . Backpropagation ( Rumelhart et al. , 1985 ) is by far the most common method used to train neural networks . Alternatives to backpropagation are typically used only when backpropagation is impractical due to a non-differentiable loss ( Schulman et al. , 2015 ) , non-smooth loss landscape ( Metz et al. ... | It is a very poorly written paper. Basic idea of finding a way to not have to wait for full forward pass is not new. Multiple research papers have been published from the extreme of using stale weight to some form of sub-network backdrop as a proxy for the full network. This paper proposed no new idea for local update.... | SP:ad7eb2bcb3a83153f140e5e8bfaa8b76110e62ab |
Simple and Effective VAE Training with Calibrated Decoders | 1 INTRODUCTION . Deep density models based on the variational autoencoder ( VAE ) ( Kingma & Welling , 2014 ; Rezende et al. , 2014 ) have found ubiquitous use in probabilistic modeling and representation learning as they are both conceptually simple and are able to scale to very complex distributions and large dataset... | This paper discusses a well-known problem of VAE training that decoder produces blurry reconstruction with constant variance. While much existing work addressed this problem by introducing independent variance training (as of the original VAE model) or additional hyper-parameters, those approaches usually come with add... | SP:a3e5acdd322677d019a4582db78dab2dc1102818 |
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation | 1 INTRODUCTION . Uncertainty evaluation is a core technique in practical applications of deep neural networks ( DNNs ) . As an example , let us consider the Cyber-Physical Systems ( CPS ) such as the automated driving system . In the past decade , machine learning methods are widely utilized to realize the environment ... | This paper proposes a sampling free technique based on variance propagation to model predictive distributions of deep learning models. Estimating uncertainty of deep learning models is an important line of research for understanding the reliability of predictions and ensuring robustness to out-of-distribution data. Res... | SP:3a1d7f7165762299ba2d9bab4144576660b9a784 |
Private Post-GAN Boosting | 1 INTRODUCTION . The vast collection of detailed personal data , including everything from medical history to voting records , to GPS traces , to online behavior , promises to enable researchers from many disciplines to conduct insightful data analyses . However , many of these datasets contain sensitive personal infor... | This paper studies the differential private synthetic dataset generation. Unlike previous DP based GAN models, this paper aims to boost the sample quality of after the training stage. In particular, the final synthetic dataset is sampled from the sequence of generators obtained during GAN training. The distribution is ... | SP:72d1283f3602edc22896934271fcec5b03f25d9e |
A Near-Optimal Recipe for Debiasing Trained Machine Learning Models | 1 INTRODUCTION . Machine learning is increasingly applied to critical decisions which can have a lasting impact on individual lives , such as for credit lending ( Bruckner , 2018 ) , medical applications ( Deo , 2015 ) , and criminal justice ( Brennan et al. , 2009 ) . Consequently , it is imperative to understand and ... | In this paper, the authors propose a post-processing method for removing bias from a trained model. The bias is defined as conditional statistical parity — for a given partitioning of the data, the predicted label should be conditionally uncorrelated with the sensitive (bias inducing) attribute for each partition. The ... | SP:a6280b6605e621403de6ac4c3fc80fa71184ab6d |
DeLighT: Deep and Light-weight Transformer | 1 INTRODUCTION . Attention-based transformer networks ( Vaswani et al. , 2017 ) are widely used for sequence modeling tasks , including language modeling and machine translation . To improve performance , models are often scaled to be either wider , by increasing the dimension of hidden layers , or deeper , by stacking... | This paper presents a variant of Transformer where low-dimension matrix multiplications and single-head attention are used. Stacked group-linear-transformation (GLT) are applied on input of each layer to perform dimension growth and then reduction. The paper is well-written and easy to follow. Experiments demonstrate t... | SP:90ffef024018f59b3bde23aa2e2a4677602d41e8 |
On the mapping between Hopfield networks and Restricted Boltzmann Machines | 1 INTRODUCTION . Hopfield networks ( HNs ) ( Hopfield , 1982 ; Amit , 1989 ) are a classical neural network architecture that can store prescribed patterns as fixed-point attractors of a dynamical system . In their standard formulation with binary valued units , HNs can be regarded as spin glasses with pairwise interac... | This paper shows a relationship between the project rule weights of a Hopfield network (HN) and the interaction weights in a corresponding restricted Boltzmann machine (RBM). The mapping from HN to RBM is facilitated by realising that the partition function of BN can be seen as the partition function of a binary-contin... | SP:c83ecc74eb885df5f29e5a7080a8c60d1ee0a3b0 |
One Reflection Suffice | Orthogonal weight matrices are used in many areas of deep learning . Much previous work attempt to alleviate the additional computational resources it requires to constrain weight matrices to be orthogonal . One popular approach utilizes many Householder reflections . The only practical drawback is that many reflection... | The authors present a way to learn the action of an arbitrary orthogonal matrix on a vector via a map from $\mathbb{R}^{n\times n}$ onto $\operatorname{O}(n)$. They show that the map is surjective, and give conditions under which they can invert this action. They then compare against previous proposed schemes in one ta... | SP:3d705a1b70254d2b9d05277efff8ac08b0539086 |
PCPs: Patient Cardiac Prototypes | 1 INTRODUCTION . Modern medical research is arguably anchored around the “ gold standard ” of evidence provided by randomized control trials ( RCTs ) ( Cartwright , 2007 ) . However , RCT-derived conclusions are population-based and fail to capture nuances at the individual patient level ( Akobeng , 2005 ) . This is pr... | This paper proposes to learn patient-specific representation using patient physiological signals. The authors design a PCP representation for each patient, which is learned to agree with signals from the same patients and disagrees with the remaining patients. In the supervised part, the classifier is generated from p... | SP:0cb862cf3806c4f04d2d30f200c25841a1cb52a8 |
Activation-level uncertainty in deep neural networks | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved state-of-the-art performance in many different tasks , such as speech recognition ( Hinton et al. , 2012 ) , natural language processing ( Mikolov et al. , 2013 ) or computer vision ( Krizhevsky et al. , 2012 ) . In spite of their predictive power , DNNs are ... | Either putting the uncertainty on the weights (e.g., Bayes by BP) or on the activation (e.g., fast dropout or variants of natural-parameter networks [2,3] or Bayesian dark knowledge [4]) or both [1] have been investigated before. The idea of moving the uncertainty from the weight to the activation function is not new. ... | SP:b7a45906d972644e9d0e757a83ff50fd3ad7cde3 |
Local SGD Meets Asynchrony | 1 INTRODUCTION . In this paper , we consider the classic problem of minimizing an empirical risk , defined simply as min x∈Rd ∑ i∈ [ I ] fi ( x ) , ( 1 ) where d is the dimension , x ∈ Rd denotes the set of model parameters , [ I ] is the training set , and fi ( x ) : Rd → R is the loss on the training sample i ∈ [ I ]... | In this paper, the authors argue that the mini-batch method and local SGD method suffers generalization performance degradation for large local mini-batch size. An asynchronous method is proposed to improve the generalization performance. A sublinear convergence rate is provided for the non-convex objective. As there a... | SP:4d94ef57fdaf5f1100b6b09331d5cff5264fcdf6 |
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues | 1 INTRODUCTION . Negotiation is ubiquitous in human interaction , from e-commerce to the multi-billion dollar sales of companies . Learning how to negotiate effectively involves deep pragmatic understanding and planning the dialogue strategically ( Thompson ; Bazerman et al. , 2000b ; Pruitt , 2013 ) . Modern dialogue ... | This paper deals with the problem of natural language generation for a dialogue system involved in complex communication tasks such as negotiation or persuasion. The proposed architecture consists of two encoders: one for the utterance and the other for dialogue acts and negotiation strategies. The decoder is an RNN th... | SP:3dffd0add054e13be141cfe939e367f6f6785eb8 |
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm | 1 INTRODUCTION . Consider the unconstrained optimization problem minimize x f ( x ) ( 1 ) where f : Rn → R is an objective function that is differentiable for all x ∈ Rn , with n being the number of decision variables forming x . Let ∇xf ( x0 ) be the gradient of f ( x ) evaluated at some x0 ∈ Rn . A general quasi-Newt... | The paper studies a problem of learning step-size policy for L-BFGS algorithm. This paper falls into a general category of meta-learning algorithms that try to derive a data-driven approach to learn one of the parameters of the learning algorithm. In this case, it is the learning rate of L-BFGS. The paper is very simil... | SP:3b3e7833784c53527eb32d5f6ac8d720f9d764bd |
Uncertainty Calibration Error: A New Metric for Multi-Class Classification | 1 INTRODUCTION . Advances in deep learning have led to superior accuracy in classification tasks , making deep learning classifiers an attractive choice for safety-critical applications like autonomous driving ( Chen et al. , 2015 ) or computer-aided diagnosis ( Esteva et al. , 2017 ) . However , the high accuracy of r... | This paper proposes a new calibration error measurement named UCE (Uncertainty Calibration Error) for deep classification models. It consists in doing a calibration in order to achieve "perfect calibration" (i.e., the uncertainty provided is equivalent to the classification error at all levels in [0, 1]), relying on no... | SP:7a92beaba926a93a627208abebe4a455ae3e0400 |
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference | 1 INTRODUCTION . Bayesian inference provides a powerful framework to blend prior knowledge , data generation process and ( possibly small ) data for statistical inference . With some prior knowledge ⇢ ( distribution ) for the quantity of interest x 2 Rd , and some ( noisy ) measurement y 2 Rdy , it casts on x a posteri... | This paper presents a model and a corresponding training approach for multi-scale invertible models. The presented model is defined on multiple scales with information on finer scales being conditioned on coarser scales. Data generation is hence done sequentially from a coarser to finer scale. The authors argue that th... | SP:92d112388a1eac20c2208f0596cdfcdcca685c8f |
Meta Gradient Boosting Neural Networks | 1 INTRODUCTION . While humans can learn quickly with a few samples with prior knowledge and experiences , artificial intelligent algorithms face challenges in dealing with such situations . Learning to learn ( or metalearning ) ( Vilalta & Drissi , 2002 ) emerges as the common practice to address the challenge by lever... | This study is presented clearly, and the core idea is interesting. However, the presented novelty is limited to a globally (for all tasks) and locally (task-specific) learning paradigm using a framework inspired by (Badirli et al., 2020). The authors have presented experimental results for both regression and classifi... | SP:077926a214f87b9fdcd5a5f9d818d6313437cd90 |
Test-Time Adaptation and Adversarial Robustness | 1 INTRODUCTION . There is a surge of interest to study test-time adaptation to help generalization to unseen domains ( e.g. , recent work by Sun et al . ( 2020 ) ; Wang et al . ( 2020 ) ; Nado et al . ( 2020 ) ) . At the high level , a generic test-time adaptation can be modeled as an algorithm Γ which accepts an ( opt... | The paper explores adversarial robustness in a new setting of test-time adaptation. It shows this new problem of “test-time-adapted adversarial robustness” is strictly weaker than the “traditional adversarial robustness” when assuming the training data is available for the “test-time-adapted adversarial robustness”. Th... | SP:2969ff98eb93abe37242a962df458541311090ff |
Subspace Clustering via Robust Self-Supervised Convolutional Neural Network | 1 INTRODUCTION . Subspace clustering approaches have achieved encouraging performance when compared with the clustering algorithms that rely on proximity measures between data points . The main idea behind the subspace model is that the data can be drawn from low-dimensional subspaces which are embedded in a high-dimen... | This paper presents an approach to deep subspace clustering based on minimizing the correntropy induced metric (CIM), with the goal of establishing when training should be stopped and generalizing to unseen data. The main contribution over the existing S2ConfSCN method is a change from squared error loss to CIM when op... | SP:b7532fd6e281d88fff5a0a89c73ae3e6651f8827 |
UNSUPERVISED ANOMALY DETECTION FROM SEMANTIC SIMILARITY SCORES | 1 INTRODUCTION . Anomaly detection or novelty detection aims at identifying patterns in data that are significantly different to what is expected . This problem is inherently a binary classification problem that classifies examples either as in-distribution or out-of-distribution , given a sufficiently large sample fro... | The authors present a new Algorithm for performing unsupervised anomaly detection in diverse applications such as visual, audio and text data. They propose a two-step method in which first they utilise contrastive learning in order to find a semantically dense map of the data onto the unit-hypersphere. Then, they class... | SP:f0e0d909df518f25eb9243837939225d7db1196e |
Learning to Generate 3D Shapes with Generative Cellular Automata | 1 INTRODUCTION . Probabilistic 3D shape generation aims to learn and sample from the distribution of diverse 3D shapes and has applications including 3D contents generation or robot interaction . Specifically , learning the distribution of shapes or scenes can automate the process of generating diverse and realistic vi... | The paper proposes a generative method for 3D objects (voxels representation). Given an initial voxels configuration (e.g. partial shape, or even a single voxel), the method learns a local transition kernel for a Markov chain to decide how to evolve the configuration; sampling iteratively from these probabilities leads... | SP:7c44bf5a4a8d5e5ee1e86ee4582c42186e2df72c |
Decentralized Deterministic Multi-Agent Reinforcement Learning | [ Zhang , ICML 2018 ] provided the first decentralized actor-critic algorithm for1 multi-agent reinforcement learning ( MARL ) that offers convergence guarantees . In2 that work , policies are stochastic and are defined on finite action spaces . We extend3 those results to offer a provably-convergent decentralized acto... | This paper extends the results for actor-critic with stochastic policies of [Zhang, ICML 2018] to deterministic policies and offers the proof of convergence under some specific assumptions. The authors consider both the on-policy setting and the off-policy setting and offers some convincing derivation. It provides a va... | SP:9326f169cc5e8d2f4268dcf39af31590ee004d98 |
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning | 1 INTRODUCTION . State-of-the-art for most existing natural language processing ( NLP ) classification tasks is achieved by models that are first pre-trained on auxiliary language modeling tasks and then fine-tuned on the task of interest with cross-entropy loss ( Radford et al. , 2019 ; Howard & Ruder , 2018 ; Liu et ... | The paper proposes a new training objective for fine-tuning pre-trained models: a weighted sum of the classical cross-entropy (CE) and a new supervised contrastive learning term (SCP). The latter uses the (negated) softmax over the embedding distances (i.e. dot products) between a training instance and all other instan... | SP:cc282126b689c7311c3a28f0d173a004ed24382f |
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution | Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards . Complex tasks are often hierarchically composed of sub-tasks . Solving a sub-task increases the return expectation and leads to a step in the Q-function . RUDDER identifies these steps and then r... | Paper proposes to attack the challenging problem of RL with sparse feedback by leveraging a few demonstrations and learnable reward redistribution. The redistributed reward is computed by aligning the key events (a set of clustered symbols) to the demonstrations via PSSM-based seq matching. Experiments on two artificia... | SP:7eb0d8278168465270570233e4af64ebb3f2f154 |
ChePAN: Constrained Black-Box Uncertainty Modelling with Quantile Regression | 1 INTRODUCTION . The present paper proposes a novel method for adding aleatoric uncertainty estimation to any pointwise predictive system currently in use . Considering the system as a black box , i.e . avoiding any hypothesis about the internal structure of the system , the method offers a solution to the technical de... | This paper proposes a novel approach to modeling uncertainty, as an layer added-on to an otherwise black-box system. The ChePAN uses a neural network to estimate per-quantile roots of a chebyshev polynomial, then uses a quantile regression loss to fit these coefficients using backpropagation. Importantly, the Chebyshev... | SP:233335a3dc327cf153bd2e8d35a9e4594cf5bc67 |
Towards Robust and Efficient Contrastive Textual Representation Learning | 1 INTRODUCTION . Representation learning is one of the pivotal topics in natural language processing ( NLP ) , in both supervised and unsupervised settings . It has been widely recognized that some forms of “ general representation ” exist beyond specific applications ( Oord et al. , 2018 ) . To extract such generaliza... | This paper proposes an approach to improve (supervised and unsupervised) representation learning for text using constrastive learning. The proposed approach augments standard contrastive learning with: (1) Spectral-norm regularization of the critic to estimate the Wasserstein distance instead of the KL (as in the Wasse... | SP:eff774eddcc60e943c0a41207c21a1c9d6d5d950 |
Progressive Skeletonization: Trimming more fat from a network at initialization | 1 INTRODUCTION . The majority of pruning algorithms for Deep Neural Networks require training dense models and often fine-tuning sparse sub-networks in order to obtain their pruned counterparts . In Frankle & Carbin ( 2019 ) , the authors provide empirical evidence to support the hypothesis that there exist sparse sub-... | The paper finds that at extreme sparsities (>95%), existing approaches to pruning neural networks at initialization devolve to worse than random pruning. The paper posits that this degenerate behavior is due to the fact that weights are pruned in groups, though the saliency metrics only capture pointwise changes. The p... | SP:a8bb14b514e474691be63b51582544a9befa7125 |
Communication in Multi-Agent Reinforcement Learning: Intention Sharing | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved remarkable success in various complex control problems such as robotics and games ( Gu et al . ( 2017 ) ; Mnih et al . ( 2013 ) ; Silver et al . ( 2017 ) ) . Multi-agent reinforcement learning ( MARL ) extends RL to multi-agent systems , which model many pract... | Paper proposed to generate the communication message in MARL with the predicted trajectories of all the agents (include the agent itself). An extra self-attention model is also stacked over the trajectories to trade off the length of prediction and the possible explaining away issue. The whole model is trained via a c... | SP:ee89d3273df8b3b082c0e72a8768dff7cd3b7f56 |
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks | 1 INTRODUCTION . We are exceedingly reliant on our mobile devices in our everyday lives . Numerous activities , such as banking , communications , and information retrieval , have gone from having separate channels to collapsing into one : through our mobile phones . While this has made many of our lives more convenien... | In this paper, the authors focus on keystroke inference attacks in which an attacker leverages machine learning approaches, In particular, a new framework is proposed for low-resource video domain adaptation using supervised disentangled learning, and another method to assess the threat of keystroke inference attacks ... | SP:b24e79d30d19c99f1093779bdba8bd8b2aed9ec0 |
Warpspeed Computation of Optimal Transport, Graph Distances, and Embedding Alignment | 1 INTRODUCTION . Measuring the distance between two distributions or sets of objects is a central problem in machine learning . One common method of solving this is optimal transport ( OT ) . OT is concerned with the problem of finding the transport plan for moving a source distribution ( e.g . a pile of earth ) to a s... | The paper considers the problem of approximating Sinkhorn divergence and corresponding transportation plan by combining low-rank and sparse approximation for the Sinkhorn kernel and using Nystrom iterations as a substitute for Sinkhorn's iterations. The corresponding approach is amenable to differentiation and can be u... | SP:181ce6eaacf4be8ede3fbdd82c63200278f63cc4 |
Adversarial score matching and improved sampling for image generation | 1 INTRODUCTION . Song and Ermon ( 2019 ) recently proposed a novel method of generating samples from a target distribution through a combination of Denoising Score Matching ( DSM ) ( Hyvärinen , 2005 ; Vincent , 2011 ; Raphan and Simoncelli , 2011 ) and Annealed Langevin Sampling ( ALS ) ( Welling and Teh , 2011 ; Robe... | The submission presents three contributions. First, the authors show the inconsistencies in the existing annealed Langevin sampling used in score-matching generative models and propose to correct it with the newly proposed Consistent Annealed Sampling (CAS) algorithm. The second contribution claimed is in providing evi... | SP:06414ad3c4b2438227a6d0749755106ee30f1564 |
Collaborative Filtering with Smooth Reconstruction of the Preference Function | The problem of predicting the rating of a set of users to a set of items in a recommender system based on partial knowledge of the ratings is widely known as collaborative filtering . In this paper , we consider a mapping of the items into a vector space and study the prediction problem by assuming an underlying smooth... | This paper proposes an approach based on Fourier transforms to predict ratings in collaborative filtering problems. The paper’s scope (“smooth reconstruction functions”) gets immediately narrowed down to Fourier transforms--it would be nice to provide some motivation for this choice over alternative smooth functions. T... | SP:f61e427d087e7f8b176a518af6088bde2ab75167 |
Robust Pruning at Initialization | 1 INTRODUCTION . Overparameterized deep NNs have achieved state of the art ( SOTA ) performance in many tasks ( Nguyen and Hein , 2018 ; Du et al. , 2019 ; Zhang et al. , 2016 ; Neyshabur et al. , 2019 ) . However , it is impractical to implement such models on small devices such as mobile phones . To address this prob... | The theoretical analysis is clearly stated in an well-organized way and the derived sparsity bound is reasonable. With FFNN and CNN, a theorem is given to show that the model is trainable only when the initialization on Edge of Chaos (EOC) and also provided a rescaling method to make the pruned NN into EOC regime. With... | SP:97471b69a8e0ce6d2bbb202cc3f9cd786e77ddea |
Does Adversarial Transferability Indicate Knowledge Transferability? | Despite the immense success that deep neural networks ( DNNs ) have achieved , adversarial examples , which are perturbed inputs that aim to mislead DNNs to make mistakes , have recently led to great concerns . On the other hand , adversarial examples exhibit interesting phenomena , such as adversarial transferability ... | This paper study the fundamental relationship between adversarial transferability and knowledge transferability. Theoretical analysis is conducted, revealing that adversarial transferability can indicate knowledge transferability. In this procedure, two quantities are formally defined to measure adversarial transferabi... | SP:934bf46c7ff0d3a3b1f0b75e48235dd0c902558c |
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation | 1 INTRODUCTION . Text generation has been one of the most important research problems in natural language processing ( NLP ) ( Reiter & Dale , 2000 ) . Thanks to the advances in neural architectures , models are now capable of generating texts that are of better quality than before ( Brown et al. , 2020 ) . However , d... | This work proposes an effective modification of language model token-level distribution during the training which prevents some forms of degeneration such as repetitions and dullness. The approach is based on the idea of encouraging the model to use tokens which were not observed in the previous context so far. In othe... | SP:4a6f5bb1d0f72df5782a09a1ffc5e19504010e36 |
Learning Representations by Contrasting Clusters While Bootstrapping Instances | 1 INTRODUCTION . Learning to extract generalized representations from a high-dimensional image is essential in solving various down-stream tasks in computer vision . Though a supervised learning framework has shown to be useful in learning discriminative representations for pre-training the model , expensive labeling c... | In this paper, the authors augment the instance-level self-supervised learning with cluster-aware learning mechanism during the training procedure. Specifically, for each training batch, the authors project the instances into a clustering space and then utilize a cluster-aware contrastive loss to push the augmented sam... | SP:2062ab9c65e0d10e5d6d0112aaeaca208f131afd |
Understanding the failure modes of out-of-distribution generalization | 1 INTRODUCTION . A machine learning model in the wild ( e.g. , a self-driving car ) must be prepared to make sense of its surroundings in rare conditions that may not have been well-represented in its training set . This could range from conditions such as mild glitches in the camera to strange weather conditions . Thi... | The paper studies generalization under distribution shift, and tries to answer the question: why do ERM-based classifiers learn to rely on "spurious" features? They present a class of distributions called "easy-to-learn" that rules out several explanations given in recent work and isolates the spurious correlation phen... | SP:b47032cd0c8bf0189504e1c6562b058ba8f0e8ae |
TaskSet: A Dataset of Optimization Tasks | We present TaskSet , a dataset of tasks for use in training and evaluating optimizers . TaskSet is unique in its size and diversity , containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks , to variational autoencoders , to non-volume preserving flows on... | This paper proposes a dataset of tasks to help evaluate learned optimizers. The learned optimizers are evaluated by the loss that they achieve on held-out tasks after 10k steps. Using this dataset, the main strategy considered is to use search spaces that parametrize optimizers and learn a list of hyperparameter config... | SP:698104525f6955ba58aee1331a9487f77a542f13 |
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization | In this work , we examine the security of InstaHide , a scheme recently proposed by Huang et al . ( 2020b ) for preserving the security of private datasets in the context of distributed learning . To generate a synthetic training example to be shared among the distributed learners , InstaHide takes a convex combination... | The purpose of the paper seems clear: it proposes an attack to the recently proposed algorithm called Instahide (ICML 2020) which is a probabilistic algorithm for generating synthetic private data in the distributed setting. The attack proposed in this paper is considered for the case where the private data is i.i.d. G... | SP:4bda50ce81c790cf9b19a24d81db4c07ec3729c1 |
Towards Noise-resistant Object Detection with Noisy Annotations | 1 INTRODUCTION . The remarkable success of modern object detectors largely relies on large-scale datasets with extensive bounding box annotations . However , it is extremely expensive and time-consuming to acquire high-quality human annotations . For example , annotating each bounding box in ILSVRC requires 42s on Mech... | In this work the authors propose a framework to perform object detection when there is noise present in class labels as well as bounding box annotations. The authors propose a two-step process, where in the first step the bounding boxes are corrected in class-agnostic way, and in the second step knowledge distillation ... | SP:a1c54d5c42097b8ba971ac20470de864ae87dd4e |
Self-Supervised Learning of Compressed Video Representations | Self-supervised learning of video representations has received great attention . Existing methods typically require frames to be decoded before being processed , which increases compute and storage requirements and ultimately hinders largescale training . In this work , we propose an efficient self-supervised approach ... | This paper proposes an approach to self-supervised learning from videos. The approach takes advantage of compressed videos, using the encoded residuals and motion vectors within the video codec. Using encoded videos has been shown to reduce computation time required by decoding videos. Previous works have explored comp... | SP:4fde35c9931ca15ab6cd53b171323e1abf0224db |
Provably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning | 1 INTRODUCTION . Bilevel optimization has received significant attention recently and become an influential framework in various machine learning applications including meta-learning ( Franceschi et al. , 2018 ; Bertinetto et al. , 2018 ; Rajeswaran et al. , 2019 ; Ji et al. , 2020a ) , hyperparameter optimization ( Fr... | The paper presents two algorithms - one for the deterministic and one for stochastic bilevel optimization. The paper claims the methods are lower cost in computational complexity for various terms and easy to implement. A finite-time convergence proof is provided for the algorithms. Empirical results are presented for... | SP:2d804ce6cd9917277ac5c4d6c72cceeb14bf0641 |
Invertible Manifold Learning for Dimension Reduction | 1 INTRODUCTION . In real-world scenarios , it is widely believed that the loss of data information is inevitable after dimension reduction ( DR ) , though the goal of DR is to preserve as much information as possible in the low-dimensional space . In the case of linear DR , compressed sensing ( Donoho , 2006 ) breaks t... | In this paper, the authors propose a novel manifold learning method, via adding a locally isometric smoothness constraint, which preserves topological and geometric properties of data manifold. Empirical results demonstrate the efficacy of their approach. The authors also show that the reliability of tangent space appr... | SP:2c5537aa2c173582e193c903eb85dd63aabc7366 |
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | 1 INTRODUCTION . Self-attention-based architectures , in particular Transformers ( Vaswani et al. , 2017 ) , have become the model of choice in natural language processing ( NLP ) . The dominant approach is to pre-train on a large text corpus and then fine-tune on a smaller task-specific dataset ( Devlin et al. , 2019 ... | This paper introduces a Transformer-based image recognition model that is fully built on the Transformer layers (multi-head self-attention + point-wise MLP) without any standard convolution layers. Basically, it splits an image into patches and takes as input the set of linear embeddings of the patches and their positi... | SP:26c214e61671b012baa8824a39772738a861e44b |
Learning representations from temporally smooth data | 1 INTRODUCTION . Events in the world are correlated in time : the information that we receive at one moment is usually similar to the information that we receive at the next . For example , when having a conversation with someone , we see multiple samples of the same face from different angles over the course of severa... | Temporal smoothness is a recurring feature of real-world data that has been unaccounted for when training neural networks. Much of the random sampling in training neural networks is done to remove the temporal correlations originally present when the data is collected. This work aims to propose a method to train on thi... | SP:87507439ef121d5d243502d2cb45eafec175f2bc |
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient | 1 INTRODUCTION . We consider the problem of black-box optimization , where we search for the optima of a loss function F : Rd → R given access to only its function queries . This type of optimization finds applications in many machine learning areas where the loss function ’ s gradient is inaccessible , or unuseful , f... | The authors study the problem of global non-convex optimization with access only to function valuations. Specifically, they propose an approach to automatically control the hyper-parameters of Directional Gaussian Smoothing (DGS) a recently proposed solution for the problem. Their proposed solution trade-offs some addi... | SP:d460957c05007cafe286b0590ffed111c806dd48 |
End-to-end Quantized Training via Log-Barrier Extensions | 1 INTRODUCTION . As state-of-the-art deep learning models for vision , language understanding and speech grow increasingly large and computationally burdensome ( He et al. , 2017 ; Devlin et al. , 2018 ; Karita et al. , 2019 ) , there is increasing antithetical demand , motivated by latency , security and privacy conce... | The paper is addressing an important and challenging problem of end-to-end training of deep nets in fixed-point, in this case, with 8-bit precision. A good solution to this problem can have a major impact on the deployability of deep nets on embedded hardware. The basic idea is to introduce an additional term (the log-... | SP:253566b5271d22d4d6492ef9def2e67fb99c5d57 |
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention | 1 INTRODUCTION AND RELATED WORK . Most of supervised machine learning has been developed with the assumption that the distribution of classes seen at train and test time are the same . However , the real-world is unpredictable and open-ended , and making machine learning systems robust to the presence of unknown catego... | I read this paper with great interest. The authors propose an easy-to-understand, easy-to-implement baseline method for detecting when inputs to a ML model is out of distribution. The method involves augmenting the training dataset with an out of distribution dataset and adding an additional class in the classificatio... | SP:d9155553fae947cc53d87a221fdd1d57b44f5ec6 |
Outlier Robust Optimal Transport | 1 INTRODUCTION . Optimal transport is a fundamental problem in applied mathematics . In its original form ( Monge , 1781 ) , the problem entails finding the minimum cost way to transport mass from a prescribed probability distribution µ on X to another prescribed distribution ν on X . Kantorovich ( 1942 ) relaxed Monge... | The authors propose to address the robustness over outliers for optimal transport (OT). They propose a new formulation based on penalizing the contaminated probability measures by a signed measure (which shares a close relation with unbalanced OT). The authors further derive an equivalent formulation by adjusting the c... | SP:70b8c75426f18a3dc4a359c8a8cd7dd2076953a0 |
Dependency Structure Discovery from Interventions | 1 INTRODUCTION . Structure learning concerns itself with the recovery of the graph structure of Bayesian networks ( BNs ) from data samples . A natural application of Bayesian networks is to describe cause-effect relationships between variables . In that context , one may speak of causal structure learning . Causal str... | This paper aims to extend the continuous optimization approach to causal discovery to handle interventional data as well as observational data. It describes a method for learning the causal structure over a set of categorical variables and reports strong empirical performance. However, no theoretical guarantee or analy... | SP:198d7f650c930a1423f7f30688cd2f73d2719920 |
Improved Autoregressive Modeling with Distribution Smoothing | 1 INTRODUCTION . Autoregressive models have exhibited promising results in a variety of downstream tasks . For instance , they have shown success in compressing images ( Minnen et al. , 2018 ) , synthesizing speech ( Oord et al. , 2016a ) and modeling complex decision rules in games ( Vinyals et al. , 2019 ) . However ... | .** Autoregressive models have demonstrate their potential utility for modeling images and other types of complex data with high flexibility (particularly in density estimation). However, its sampling ability is not that good as explained in the paper. Authors show that one of the main weaknesses of autoregressive mode... | SP:d8c4980cf2187b549f2f2a4fbb2fba4101337459 |
CorrAttack: Black-box Adversarial Attack with Structured Search | We present a new method for score-based adversarial attack , where the attacker queries the loss-oracle of the target model . Our method employs a parameterized search space with a structure that captures the relationship of the gradient of the loss function . We show that searching over the structured space can be app... | This work considers an important problem of generating adversarial examples to attack a black-box model. The paper proposes a new approach to consider an adversarial example as a result of a sequence of pixel changes from a benign instance. Therefore, the adversarial generation problem can be considered as a bandit pro... | SP:5918a2c105a901f8de4bba248dc283a476d9beac |
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima | 1 INTRODUCTION . In recent years , deep learning ( LeCun et al. , 2015 ) has achieved great empirical success in various application areas . Due to the over-parametrization and the highly complex loss landscape of deep networks , optimizing deep networks is a difficult task . Stochastic Gradient Descent ( SGD ) and its... | The paper develops a density diffusion theory to reveal how minima selection quantitatively depends on the minima sharpness and the hyperparameters. It shows theoretically and empirically that SGD favors flat minima exponentially more than sharp minima. In particular, the paper analyzed the dependence of mean escape ti... | SP:9403fa2679f18af78aed2e81b75eb39abeb722eb |
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs | 1 INTRODUCTION . Combinatorial optimization problems , such as shortest path in a weighted directed graph , minimum spanning tree in a weighted undirected graph , or optimal assignment of tasks to workers , play a central role in many computer science applications . We have highly refined , efficient algorithms for sol... | The value of the optimal objective as a function of the cost vector $c$ can be written as $z^*(c) = c^T u^*(c)$ where the optimal solution $u^*$ also depends on $c$. The function $u^*(c)$ is piecewise constant -- there are finitely (resp. countably) many feasible solutions; candidates for $u^*$ -- and so the function $... | SP:d92fe94e29672783f906710a2ecb7a02aa4bd67d |
Efficient Differentiable Neural Architecture Search with Model Parallelism | 1 INTRODUCTION . Neural architecture search ( NAS ) has revolutionized architecture designs of deep learning from manually to automatically in various applications , such as image classification ( Zoph & Le , 2016 ) and semantic segmentation ( Liu et al. , 2019a ) . Reinforcement learning ( Zoph & Le , 2016 ; Zoph et a... | This paper provides the interesting method that leverages GPU memory resources more efficiently for supernet (meta-graph) of differentiable NAS. For this, this paper proposes binary neural architecture search and consecutive model parallel (CMP). CMP parallelizes one supernet with multiple GPUs, which allows NAS model ... | SP:16d9ab54eb8e4f24314ceca6e0f86f4ca586d7f1 |
Video Prediction with Variational Temporal Hierarchies | 1 INTRODUCTION Deep learning has enabled predicting video sequences from large datasets ( Chiappa et al. , 2017 ; Oh et al. , 2015 ; Vondrick et al. , 2016 ) . For high-dimensional inputs such as video , there likely exists a more compact representation of the scene that facilitates long term prediction . Instead of le... | This paper proposes a method called Temporal Abstract Latent Dynamics (TALD). TALD is built up on RSSM (Hafner et al. 2019) but with hierarchical dynamics. The experiments are conducted on moving MNIST, GQN 3D Mazes, and KTH. Results are qualitatively better than other methods in term of maintaining long-term consisten... | SP:d10957cc11891e1aad6ecac21a73d589bfac341d |
Disentangled Recurrent Wasserstein Autoencoder | 1 INTRODUCTION . Unsupervised representation learning is an important research topic in machine learning . It embeds high-dimensional sensory data such as images and videos into a low-dimensional latent space in an unsupervised learning framework , aiming at extracting essential data variation factors to help downstrea... | This paper extends the Wasserstein autoencoder for learning disentangled representations from sequential data. The latent variable model considered contains separate latent variables capturing global and local information respectively, each of which is regularized by a divergence measuring the marginal posterior $Q_z$ ... | SP:6082a5b51b24315dfdbfe147de1aef2c53cd113d |
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs | 1 INTRODUCTION . Deep learning has seen stunning successes in the last decade in computer vision ( Krizhevsky et al. , 2012 ; Szegedy et al. , 2015 ) , natural language processing and translation ( Vaswani et al. , 2017 ; Radford et al. , 2019 ; Kaplan et al. , 2020 ) , and computer game playing ( Mnih et al. , 2015 ; ... | The paper extends prior work on equivalence between predictive coding and backprop in layered neural networks to arbitrary computation graphs. This is empirically tested first on a simple nonlinear scalar function, and then on a few commonly used architectures (CNNs, RNNs, LSTMs), confirming the theoretical results. Th... | SP:60894f74f40addd7a2a35a003dcdce6cf70ffef4 |
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art results in various tasks in natural language processing ( NLP ) tasks ( Sutskever et al. , 2014 ; Vaswani et al. , 2017 ; Devlin et al. , 2019 ) and computer vision ( CV ) tasks ( He et al. , 2016 ; Goodfellow et al. , 2016 ) . One approach to improve... | This paper proposes a simple scheme for training with multiple augmentations of training data in one iteration and reweighting the instances by their relative loss. As authors note in their related works, the idea of reweighting examples based on their relative loss has been widely studied in a variety of machine learn... | SP:9e6b5b7d9e7459c015130f4b80f7bc75424de050 |
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