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Multi-Agent Imitation Learning with Copulas | 1 INTRODUCTION . Recent years have witnessed great success of reinforcement learning ( RL ) for single-agent sequential decision making tasks . As many real-world applications ( e.g. , multi-player games ( Silver et al. , 2017 ; Brown & Sandholm , 2019 ) and traffic light control ( Chu et al. , 2019 ) ) involve the par... | The paper proposes a multi-agent imitation learning method that learns a joint policy for all agents from offline demonstrations. The key idea is to first learn a marginal policy for each agent using behavioral cloning, then fit a copula function that captures dependencies between the agents' policies. Experiments with... | SP:900ada28e9a27c6ad856871ad5f04c50dc95e023 |
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification | 1 INTRODUCTION . Many fundamental machine learning tasks involve solving empirical risk minimization ( ERM ) : given a loss function ` , find a model w ∈ Rp that minimizes the empirical risk L̂n ( w ) = 1 n ∑n i=1 ` ( w , zi ) , where z1 , . . . , zn are i.i.d . examples drawn from a distribution P . In many applicatio... | The paper considers the problem of solving differentially private empirical risk minimization. To reduce the dependence on dimensionality $p$, they propose Projected DP-SGD (PDP-SGD) that projects the noisy gradients to a low-dimensional subspace computed from a free public dataset at each iteration. They prove that PD... | SP:d0393690be2cffce0618681c819a41cee75f1434 |
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification | 1 INTRODUCTION . Many fundamental machine learning tasks involve solving empirical risk minimization ( ERM ) : given a loss function ` , find a model w ∈ Rp that minimizes the empirical risk L̂n ( w ) = 1 n ∑n i=1 ` ( w , zi ) , where z1 , . . . , zn are i.i.d . examples drawn from a distribution P . In many applicatio... | The author proposes an algorithm that improves DPSGD by using public data to identify a lower-dimensional space where the gradients lie in. They show that the algorithm can provide a better convergence guarantee, specifically, p reduced to log(p). They also conducted experiments to show the proposed algorithm outperfor... | SP:d0393690be2cffce0618681c819a41cee75f1434 |
On the Importance of Looking at the Manifold | 1 INTRODUCTION . The ability to recognize relational information between or even within individual percepts is one of the fundamental differences between human and artificial learning systems . For example , the feature-binding problem ( Roskies , 1999 ) , i.e . the mechanism governing the visual system to represent hi... | This paper investigates different ways of incorporating topological information about the data in the machine learning models. The paper introduces a novel loss that aims to enforce the relational information between data points into the embedding space learned by a Vae on the node features. The experiments demonstrate... | SP:bc5907a21b9d31cda4e4c4c513792a8f99edd0b6 |
On the Importance of Looking at the Manifold | 1 INTRODUCTION . The ability to recognize relational information between or even within individual percepts is one of the fundamental differences between human and artificial learning systems . For example , the feature-binding problem ( Roskies , 1999 ) , i.e . the mechanism governing the visual system to represent hi... | The paper focuses on studying the importance of utilising manifold/topology information for machine learning tasks. To this end, the authors benchmark four different approaches, including VAE, GR-VAE (using graph distances to regularise embedding distances (as shown in Eq. 1)). The paper performs experiments on four ... | SP:bc5907a21b9d31cda4e4c4c513792a8f99edd0b6 |
Implicit Regularization Effects of Unbiased Random Label Noises with SGD | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) has been widely used as an effective way to train deep neural networks with large datasets ( Bottou , 1991 ) . While the mini-batch sampling strategy was firstly proposed to lower the cost of computation per iteration , it has been consider to incorporate an implicit... | This paper studies the implicit regularization effect that arises from using stochastic gradient descent with label noise and squared loss. They derive the expression for the implicit regularization term and show that it favors solutions which are stable against perturbations of the parameters. This paper validates the... | SP:55e4c0663d63e508027c4ac1a75025ffeabac81b |
Implicit Regularization Effects of Unbiased Random Label Noises with SGD | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) has been widely used as an effective way to train deep neural networks with large datasets ( Bottou , 1991 ) . While the mini-batch sampling strategy was firstly proposed to lower the cost of computation per iteration , it has been consider to incorporate an implicit... | The paper studies the implicit regularization effect of unbiased random label noise on the dynamics of stochastic gradient descent(SGD). More precisely, an unbiased random noise is added to the true labels and the paper aims to analyze the regularization effect of it. The paper shows that the unbiased label noise would... | SP:55e4c0663d63e508027c4ac1a75025ffeabac81b |
Scalable Transfer Learning with Expert Models | 1 INTRODUCTION . Deep learning has been successful on many computer vision tasks . Unfortunately , this success often requires a large amount of per-task data and compute . To scale deep learning to new vision tasks , practitioners often turn to transfer learning . Transfer learning involves re-using models trained on ... | This paper presents a novel method for obtaining better representations for transfer learning. Specifically, instead of using a generic representation for various down-stream tasks, this paper proposed to create a family of expert models in pre-training, and selectively choose one expert to generate representation depe... | SP:ee3f9c45e31d0e04b40d2547215df4e4d34e0e0d |
Scalable Transfer Learning with Expert Models | 1 INTRODUCTION . Deep learning has been successful on many computer vision tasks . Unfortunately , this success often requires a large amount of per-task data and compute . To scale deep learning to new vision tasks , practitioners often turn to transfer learning . Transfer learning involves re-using models trained on ... | The authors address transfer learning scenarios. In particular, the authors resort to training to a diverse set of experts and "cheap" performance proxies to select, for a given task, the relevant expert. This "per-task routing" is conducted via a nearest neighbor classifier based on a reduced representation for each e... | SP:ee3f9c45e31d0e04b40d2547215df4e4d34e0e0d |
Predicting Infectiousness for Proactive Contact Tracing | 1 INTRODUCTION . Until pharmaceutical interventions such as a vaccine become available , control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing . While these have often been successful in limiting spread of the disease in the short term , these restrictive meas... | In this paper the authors propose a novel method of contact tracing which they dub Proactive Contact Tracing (PCT). PCT is aimed at detecting an individual's infectivity given locally observed information (and history), as can be carried on say a mobile device. As the name suggests, being able to estimate infectivity g... | SP:8d80ab2c84cae404e404a09f674bf9335f564975 |
Predicting Infectiousness for Proactive Contact Tracing | 1 INTRODUCTION . Until pharmaceutical interventions such as a vaccine become available , control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing . While these have often been successful in limiting spread of the disease in the short term , these restrictive meas... | This paper introduces a deep learning based digital contact tracing method to minimize the spread of COVID19. The proposed method is based on locally processed information collected on the mobile app. Unlike the most commonly used digital tracing approach that sends quarantine recommendations to all recent contacts of ... | SP:8d80ab2c84cae404e404a09f674bf9335f564975 |
Noise or Signal: The Role of Image Backgrounds in Object Recognition | 1 INTRODUCTION . Object recognition models are typically trained to minimize loss on a given dataset , and evaluated by the accuracy they attain on the corresponding test set . In this paradigm , model performance can be improved by incorporating any generalizing correlation between images and their labels into decisio... | The submission performs similar foreground-background analysis for object recognition as in [1], but with more modern networks in mind. As such, the main takeaways indicate that this phenomenon still exists - networks today continue to suffer from background bias as they did four years ago with AlexNet, although maybe ... | SP:0249d3ffcb3333fe857d83bae77e709a2f997956 |
Noise or Signal: The Role of Image Backgrounds in Object Recognition | 1 INTRODUCTION . Object recognition models are typically trained to minimize loss on a given dataset , and evaluated by the accuracy they attain on the corresponding test set . In this paradigm , model performance can be improved by incorporating any generalizing correlation between images and their labels into decisio... | The authors presented a comprehensive study on the role of background in image classification. They designed a new set of data and a lot of experiments to find answers to the following questions: (1) How much decrease in classification accuracy if the background signal is removed? (2) Can a model successfully classify ... | SP:0249d3ffcb3333fe857d83bae77e709a2f997956 |
Q-Value Weighted Regression: Reinforcement Learning with Limited Data | Sample efficiency and performance in the offline setting have emerged as among the main challenges of deep reinforcement learning . We introduce Q-Value Weighted Regression ( QWR ) , a simple RL algorithm that excels in these aspects . QWR is an extension of Advantage Weighted Regression ( AWR ) , an off-policy actor-c... | This paper presents a Q-value weighted regression (QWR) on top of the advantage weighted regression (AWR) to improve the sample efficiency for offline RL settings. Through the analysis to the AWR, the authors claim that it performs poorly in scenarios with discrete actions, which motivates the development of QWR. Empir... | SP:f9d1d627589f50a87c503a6df484145dc01ebcca |
Q-Value Weighted Regression: Reinforcement Learning with Limited Data | Sample efficiency and performance in the offline setting have emerged as among the main challenges of deep reinforcement learning . We introduce Q-Value Weighted Regression ( QWR ) , a simple RL algorithm that excels in these aspects . QWR is an extension of Advantage Weighted Regression ( AWR ) , an off-policy actor-c... | This paper focuses on offline policy learning with a limited dataset and proposes a sample efficient algorithm called Q-Value Weighted Regression. Based on the Advantage Weighted Regression algorithm, this algorithm calculates the advantage of the sampling policy \mu by estimated Q-value function. Experiment results sh... | SP:f9d1d627589f50a87c503a6df484145dc01ebcca |
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning | 1 INTRODUCTION . In Reinforcement Learning ( RL ) , one of the fundamental problems is exploration-exploitation dilemma , i.e. , the agents explore the states with imperfect knowledge to improve future reward or instead maximize the intermediate reward at the perfectly understood states . The main obstacle of designing... | This work introduces max-Q Entropy Search (MQES) exploration principle for continuous RL algorithms. MQES addresses the exploration-exploitation dilemma that constitutes a fundamental RL problem. Actually, MQES defines an exploration policy able to explore optimistically and avoid over-exploration. One of the main adv... | SP:0b92f455d4643fb63c1d5885558545674bf120ee |
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning | 1 INTRODUCTION . In Reinforcement Learning ( RL ) , one of the fundamental problems is exploration-exploitation dilemma , i.e. , the agents explore the states with imperfect knowledge to improve future reward or instead maximize the intermediate reward at the perfectly understood states . The main obstacle of designing... | This paper proposes MQES, a Max-Q entropy search for policy optimization in continuous RL. The authors propose to combine advantages of the information-theoretic principle and distributional RL, in which epistemic and aleatoric uncertainty are estimated using similar entropy-search acquisition functions in the Bayesian... | SP:0b92f455d4643fb63c1d5885558545674bf120ee |
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | 1 INTRODUCTION . Modularity is an important organization principle in both artificial ( Ballard , 1987 ; Baldwin & Clark , 2000 ) and biological ( von Dassow & Munro , 1999 ; Lorenz et al. , 2011 ; Clune et al. , 2013 ) systems . It provides a natural way of achieving compositionality , which appears essential for syst... | The paper presents an empirical study of whether modularity can emerge within neural networks. It starts by proposing a novel definition of modularity that identifies modules by their functionality. To discover the module that implements a specific target functionality, the paper proposes to first pretrain the full net... | SP:730c95c4ef5534877dcbd535e53dc8ba4879ed36 |
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | 1 INTRODUCTION . Modularity is an important organization principle in both artificial ( Ballard , 1987 ; Baldwin & Clark , 2000 ) and biological ( von Dassow & Munro , 1999 ; Lorenz et al. , 2011 ; Clune et al. , 2013 ) systems . It provides a natural way of achieving compositionality , which appears essential for syst... | This paper studies weight modularity in neural networks (NNs). In particular, given a NN trained to perform a task, a subset of weights are identified which in isolation perform well on a subtask of the original task. Such subsets are inspected to understand the extent to which they are specialized or reused across dif... | SP:730c95c4ef5534877dcbd535e53dc8ba4879ed36 |
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations | Graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis . Currently , several models based on mutual information maximization have shown strong performance on the task of unsupervised graph representation learning . In this paper , inst... | This paper proposes an unsupervised graph-level representation learning method considering global-local disentanglement. Specifically, the authors propose a GL-Disen model based on graph VAE architecture to jointly learn global and local representations for a graph. The global information is shared across the whole gra... | SP:47c40cbd381242ca804b1d6c6e95d04e28520733 |
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations | Graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis . Currently , several models based on mutual information maximization have shown strong performance on the task of unsupervised graph representation learning . In this paper , inst... | In this paper, the authors proposed a disentanglement learning based approach for unsupervised graph level representation learning. They assume that disentangled representations which capture these global and local generative factors into independent latent units can be highly beneficial for graph level tasks. The exte... | SP:47c40cbd381242ca804b1d6c6e95d04e28520733 |
Unified Principles For Multi-Source Transfer Learning Under Label Shifts | 1 INTRODUCTION . Transfer learning ( Pan & Yang , 2009 ) is based on the motivation that learning a new task is easier after having learned several similar tasks . By learning the inductive bias from a set of related source domains ( S1 , . . . , ST ) and then leveraging the shared knowledge upon learning the target do... | In this paper, the authors focus on the label shift problem in multi-source transfer learning and derive new generic principles to control the target generalization risk. They propose a framework that unifies the principles of conditional feature alignment, label distribution ratio estimation, and domain relation weigh... | SP:b93dae908d1cdefa8097c5c96e4829fe157b4073 |
Unified Principles For Multi-Source Transfer Learning Under Label Shifts | 1 INTRODUCTION . Transfer learning ( Pan & Yang , 2009 ) is based on the motivation that learning a new task is easier after having learned several similar tasks . By learning the inductive bias from a set of related source domains ( S1 , . . . , ST ) and then leveraging the shared knowledge upon learning the target do... | This paper aims to provide a unified principle for multi-source transfer learning under label shifts. Based on this principle, this paper claims that a unified algorithm is proposed for various multi-source label shift transfer scenarios: learning with limited target data, unsupervised domain adaptation and label parti... | SP:b93dae908d1cdefa8097c5c96e4829fe157b4073 |
Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties | 1 INTRODUCTION . Efficiently training deep neural networks has proven crucial for achieving state-of-the-art results in machine learning ( e.g . Krizhevsky et al. , 2012 ; Graves et al. , 2013 ; Karpathy et al. , 2014 ; Mnih et al. , 2015 ; Silver et al. , 2016 ; Vaswani et al. , 2017 ; Radford et al. , 2019 ; Schrittw... | This paper proposes Expectigrad, which is a new optimizer for nonconvex optimization. The main idea is to consider arithmetic mean of squared gradients instead of exponential moving average and to use a normalization factor that takes into account the number of nonzeros observed during the run of the algorithm, for eac... | SP:9c00384d154224a1d849aa254aab6eec214a75d9 |
Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties | 1 INTRODUCTION . Efficiently training deep neural networks has proven crucial for achieving state-of-the-art results in machine learning ( e.g . Krizhevsky et al. , 2012 ; Graves et al. , 2013 ; Karpathy et al. , 2014 ; Mnih et al. , 2015 ; Silver et al. , 2016 ; Vaswani et al. , 2017 ; Radford et al. , 2019 ; Schrittw... | This paper proposes the Expectigrad algorithm that normalizes the exponential moving average (EMA) of first moments on the fly. This avoids normalizing historical gradients by future gradients. The normalization factor is an unweighted average, instead of an EMA, of the historical second moments. For the special case w... | SP:9c00384d154224a1d849aa254aab6eec214a75d9 |
Diverse Video Generation using a Gaussian Process Trigger | 1 INTRODUCTION . Humans are often able to imagine multiple possible ways that the scene can change over time . Modeling and generating diverse futures is an incredibly challenging problem . The challenge stems from the inherent multi-modality of the task , i.e. , given a sequence of past frames , there can be multiple ... | The authors propose to use a Gaussian Process (GP) to model the uncertainty of future frames in a video prediction setup. In particular, they employ a GP to model the uncertainty of the next step latent in a latent variable model. This allows them to use the GP variance to decide when to change an "action sequence", co... | SP:d92978dc1a820c8d0a8eb39a3129b4e3132cafa1 |
Diverse Video Generation using a Gaussian Process Trigger | 1 INTRODUCTION . Humans are often able to imagine multiple possible ways that the scene can change over time . Modeling and generating diverse futures is an incredibly challenging problem . The challenge stems from the inherent multi-modality of the task , i.e. , given a sequence of past frames , there can be multiple ... | This paper proposes a future frame prediction framework where the video generation can transition between different actions using a Gaussian process trigger. The framework consists of three components: an encoder which encodes the frame to a latent code, an LSTM which predicts the next latent code given the current one... | SP:d92978dc1a820c8d0a8eb39a3129b4e3132cafa1 |
Adaptive and Generative Zero-Shot Learning | We address the problem of generalized zero-shot learning ( GZSL ) where the task is to predict the class label of a target image whether its label belongs to the seen or unseen category . Similar to ZSL , the learning setting assumes that all class-level semantic features are given , while only the images of seen class... | The authors proposed an interesting method for zero-shot learning. In particular, the authors adopted an attention mechanism from the input feature in the semantic to visual mapping, to introduce intra-class variations in the visual space. They also propose a process to synthesize "fake" class representations such that... | SP:1164e721c240bb98c36386b627d10626a63c1f94 |
Adaptive and Generative Zero-Shot Learning | We address the problem of generalized zero-shot learning ( GZSL ) where the task is to predict the class label of a target image whether its label belongs to the seen or unseen category . Similar to ZSL , the learning setting assumes that all class-level semantic features are given , while only the images of seen class... | The paper proposes a framework for the GZSL using the meta-learning and attention mechanism. The image-guided attention on the semantic space helps to adapt the better class specific semantic information. The modified semantic space projected to the visual space and in the visual space, cosine similarity is measured. T... | SP:1164e721c240bb98c36386b627d10626a63c1f94 |
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling | We present a novel approach to the problem of text style transfer . Unlike previous approaches that use parallel or non-parallel labeled data , our technique removes the need for labels entirely , relying instead on the implicit connection in style between adjacent sentences in unlabeled text . We show that T5 ( Raffel... | This paper proposes a novel approach to the label-free style transfer task where an input is corrupted via different strategies and fed into an auto-encoder which is additionally conditioned on its prior adjacent context sentence via a "style encoder" which adds its mean pooled hidden state to the former before decodin... | SP:d3299ff1a9838da59e00b18add7185466d239bcc |
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling | We present a novel approach to the problem of text style transfer . Unlike previous approaches that use parallel or non-parallel labeled data , our technique removes the need for labels entirely , relying instead on the implicit connection in style between adjacent sentences in unlabeled text . We show that T5 ( Raffel... | In this paper, the author proposed a transformer-based encoder-decoder framework for label-free text style transfer. The described task under the unsupervised setup is important and instructive for the text style transfer domain. The model architecture is well demonstrated and the writing is easy to follow up. The expe... | SP:d3299ff1a9838da59e00b18add7185466d239bcc |
Distributed Associative Memory Network with Association Reinforcing Loss | 1 INTRODUCTION . The essential part of human intelligence for understanding the story and predicting unobserved facts largely depends on the ability of memorizing the past and reasoning for relational information based on the pieces of memory . In this context , research on artificial intelligence has focused on design... | In this work the authors propose a novel memory architecture wherein memories are stored in multiple ways across a series of memory blocks. By "distributing" the memories in such a manner, the model can flexibly retrieve one version of a memory or another, which enables more flexible computations when conditioning on t... | SP:da88bd545609da190d45106b615b2b1bbc132279 |
Distributed Associative Memory Network with Association Reinforcing Loss | 1 INTRODUCTION . The essential part of human intelligence for understanding the story and predicting unobserved facts largely depends on the ability of memorizing the past and reasoning for relational information based on the pieces of memory . In this context , research on artificial intelligence has focused on design... | The authors propose a distributed memory architecture which shares some interface with the Differentiable Neural Computer however crucially segments memory into a collection of K units. The authors show that by increasing K the model learns to use its memory for algorithmic tasks such as copying and associative recall ... | SP:da88bd545609da190d45106b615b2b1bbc132279 |
Neural Potts Model | 1 INTRODUCTION . When two positions in a protein sequence are in spatial contact in the folded three-dimensional structure of the protein , evolution is not free to choose the amino acid at each position independently . This means that the positions co-evolve : when the amino acid at one position varies , the assignmen... | The paper proposes a new object called Neural Potts Model (NPM) to train a Transformer to learn the local energy landscape of protein sequences. The problem of modeling energy landscapes using the power of techniques in natural language processing (NLP) is a timely and interesting problem. However, there are some conce... | SP:8a7d41e17157971d368e584c9f0aa7132a361a80 |
Neural Potts Model | 1 INTRODUCTION . When two positions in a protein sequence are in spatial contact in the folded three-dimensional structure of the protein , evolution is not free to choose the amino acid at each position independently . This means that the positions co-evolve : when the amino acid at one position varies , the assignmen... | This paper aims to improve low-depth MSAs, when a protein of interest only has a small number of known evolutionarily related sequences. This is a well motivated problem. MSAs are commonly used for a variety of purposes. Methods to enhance low-depth MSAs can be very useful. In particular, this paper focuses on using MS... | SP:8a7d41e17157971d368e584c9f0aa7132a361a80 |
Improving Mutual Information based Feature Selection by Boosting Unique Relevance | 1 INTRODUCTION . High-dimensional datasets tend to contain irrelevant or redundant features , leading to extra computation , larger storage , and decreased performance ( Bengio et al. , 2013 ; Gao et al. , 2016 ; Bermingham et al. , 2015 ; Hoque et al. , 2016 ) . Mutual Information ( MI ) ( Cover & Thomas , 2006 ) base... | This work suggests improving mutual informaton based feature selection methods with an extra term (i.e., the unique relevance (UR)), and introduces a hyper-parameter $\beta$ to weight the UR. The work is easy to follow. However, the perspectives and methods are not novel. And there is a technical flaw in the analysis. | SP:121b6641093615ae9740da48399381c9ce68092e |
Improving Mutual Information based Feature Selection by Boosting Unique Relevance | 1 INTRODUCTION . High-dimensional datasets tend to contain irrelevant or redundant features , leading to extra computation , larger storage , and decreased performance ( Bengio et al. , 2013 ; Gao et al. , 2016 ; Bermingham et al. , 2015 ; Hoque et al. , 2016 ) . Mutual Information ( MI ) ( Cover & Thomas , 2006 ) base... | In this paper, the authors recognized the function of unique relevance (UR) of features for optimal feature selection and augmented the existing mutual information based feature selection (MIBFS) methods by boosting unique relevance (BUR). As a result, they proposed a new criterion called MRwMR-BUR. Experimental result... | SP:121b6641093615ae9740da48399381c9ce68092e |
Adversarial Training using Contrastive Divergence | 1 INTRODUCTION . Although deep neural networks have become increasingly popular and successful in many machine learning tasks ( e.g. , image recognition He et al . ( 2016b ) , speech recognition Hinton et al . ( 2012 ) ; van den Oord et al . ( 2016 ) and natural language processing Hochreiter & Schmidhuber ( 1997 ) ; V... | Adversarial examples are time-consuming to generate. In this paper, the adversarial training is reformulated as a combination of stationary distribution exploring, sampling, and training. A Hamiltonian system is proposed to model data samples from their initial states, and is shown as the general form of FGSM. The samp... | SP:b478b1c0c0f94040c6272af647685b0185cf16ca |
Adversarial Training using Contrastive Divergence | 1 INTRODUCTION . Although deep neural networks have become increasingly popular and successful in many machine learning tasks ( e.g. , image recognition He et al . ( 2016b ) , speech recognition Hinton et al . ( 2012 ) ; van den Oord et al . ( 2016 ) and natural language processing Hochreiter & Schmidhuber ( 1997 ) ; V... | This paper proposed a new adversarial attack method by using Markov chain Monte Carlo. Based on this attack method, a new adversarial learning method called adversarial training by using Contrastive Divergence (ATCD) which approaches equilibrium distribution of adversarial examples with only a few iterations is perfor... | SP:b478b1c0c0f94040c6272af647685b0185cf16ca |
The act of remembering: A study in partially observable reinforcement learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) agents learn policies ( i.e. , mappings from observations to actions ) by interacting with an environment . RL agents usually learn memoryless policies , which are policies that only consider the last observation when selecting the next action . In fully observable environ... | This paper focuses on reinforcement learning in partially-observable environments, and revisits the approach that consists of extending the agent with an external memory. The main contribution of the paper is the proposal (and evaluation) of adding an action to the agent, that allows it to push its current observation ... | SP:288991f341afc67f98e6318d17b06902b5488f15 |
The act of remembering: A study in partially observable reinforcement learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) agents learn policies ( i.e. , mappings from observations to actions ) by interacting with an environment . RL agents usually learn memoryless policies , which are policies that only consider the last observation when selecting the next action . In fully observable environ... | The paper extends the agent actions with an ability to write to an external memory. The paper does a nice survey of the previous approaches. The paper explains the difficulties with bootstrapping and policy improvement in POMDPs. The paper proposes simple memories for storing a buffer of k observations. The agent has t... | SP:288991f341afc67f98e6318d17b06902b5488f15 |
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks | 1 INTRODUCTION . Adversarial poisoning attacks are an important vulnerability in machine learning systems . In these attacks , an adversary can manipulate the training data of a classifier , in order to change the classifications of specific inputs at test time . Several poisoning threat models have been studied in the... | This paper studies how to enhance the robustness of classifiers in face of data poisoning attacks. The key insight of the paper is that adding or deleting one training point can at most change one of the k partitions of the training set. Based on this idea, the authors propose Deep Partition Aggregation (DPA), a robust... | SP:c496aecb8ebf5e0d803342e8582f8a2515fe344d |
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks | 1 INTRODUCTION . Adversarial poisoning attacks are an important vulnerability in machine learning systems . In these attacks , an adversary can manipulate the training data of a classifier , in order to change the classifications of specific inputs at test time . Several poisoning threat models have been studied in the... | The paper proposes to solve two variants of adversarial poisoning attacks: 1) General Poisoning Attacks - where either the input is distorted or the label is flipped. 2) Label Flipping Poisoning Attacks - where the input images are intact but only the labels are flipped. The crux of the algorithm is the standard ensemb... | SP:c496aecb8ebf5e0d803342e8582f8a2515fe344d |
TraDE: A Simple Self-Attention-Based Density Estimator | 1 INTRODUCTION . Density estimation involves estimating a probability density p ( x ) , given independent , identically distributed ( iid ) samples from it . This is a versatile and important problem as it allows one to generate synthetic data or perform novelty and outlier detection . It is also an important subroutin... | This paper proposes TraDE, a transformer-based density estimator that is capable of learning a density of real-valued tabular data. Compared to previously proposed transformers, there are three main differences in TraDE model: 1) the output is modeled as a mixture of Gaussians, 2) maximum mean discrepancy (MMD) is adde... | SP:e8c5589f22688422495fbc1dd7418139443102b9 |
TraDE: A Simple Self-Attention-Based Density Estimator | 1 INTRODUCTION . Density estimation involves estimating a probability density p ( x ) , given independent , identically distributed ( iid ) samples from it . This is a versatile and important problem as it allows one to generate synthetic data or perform novelty and outlier detection . It is also an important subroutin... | This work proposes a new auto-regressive density estimator built using self-attention module from the popular Transformer network. TraDE can be seen as an extension of decoder-only Transformer network where an input embeddings are given by a simple RNN-based encoder. Like Transformer, TraDE leverages multiple layers of... | SP:e8c5589f22688422495fbc1dd7418139443102b9 |
Active Feature Acquisition with Generative Surrogate Models | 1 INTRODUCTION . A typical machine learning paradigm for discriminative tasks is to learn the distribution of an output , y given a complete set of features , x ∈ Rd : p ( y | x ) . Although this paradigm is successful in a multitude of domains , it is incongruous with the expectations of many real-world intelligent sy... | In this work, a reinforcement learning (RL) approach is proposed to solve the active feature acquisition (AFA) problem (as well as the active instance recognition problem). Comparing to existing RL approaches for AFA, the main difference of the proposed approach is to introduce a generative model (utilizing the existin... | SP:c4549595bfdf81732bd6dbec7265f6bfed58d61b |
Active Feature Acquisition with Generative Surrogate Models | 1 INTRODUCTION . A typical machine learning paradigm for discriminative tasks is to learn the distribution of an output , y given a complete set of features , x ∈ Rd : p ( y | x ) . Although this paradigm is successful in a multitude of domains , it is incongruous with the expectations of many real-world intelligent sy... | This paper studies the problem of active feature acquisition (AFA). The authors formulate AFA as a Markov decision process (MDF) and use reinforcement learning to resolve it. In order to overcome the sparse reward and complicated action space in this situation, the authors combine a generative surrogate model into thei... | SP:c4549595bfdf81732bd6dbec7265f6bfed58d61b |
What Should Not Be Contrastive in Contrastive Learning | 1 INTRODUCTION . Self-supervised learning , which uses raw image data and/or available pretext tasks as its own supervision , has become increasingly popular as the inability of supervised models to generalize beyond their training data has become apparent . Different pretext tasks have been proposed with different tra... | The paper proposes a contrastive learning approach for self-supervised learning in which multiple heads are trained to be invariant to all but one type of data augmentation. The rationale is that different downstream tasks may require different types of invariances (e.g. we may want to be rotation invariant for picture... | SP:ec83ad5b38ab973df53f6cf9c3bad784ef6a5170 |
What Should Not Be Contrastive in Contrastive Learning | 1 INTRODUCTION . Self-supervised learning , which uses raw image data and/or available pretext tasks as its own supervision , has become increasingly popular as the inability of supervised models to generalize beyond their training data has become apparent . Different pretext tasks have been proposed with different tra... | The authors observe that, while effective, contrastive learning unavoidably introduces some bias depending on the choice of augmentations the algorithm is made invariant to, and that deteriorates performance depending on the task. The authors corroborate this hypothesis with experiments with the MoCo baseline and proce... | SP:ec83ad5b38ab973df53f6cf9c3bad784ef6a5170 |
Distributional Reinforcement Learning for Risk-Sensitive Policies | 1 INTRODUCTION . In standard reinforcement learning ( RL ) ( Sutton & Barto , 2018 ) , one seeks to learn a policy that maximizes an objective , usually the expected total discounted rewards or the long-term average rewards . In stochastic domains , especially when the level of uncertainty involved is high , maximizing... | This paper consider the problem of learning a risk-averse policy base on CVaR measure using distributional reinforcement learning. The main contributions of this paper are twofold. First, they show that the standard distributional RL algorithm overestimate the dynamic, Markovian CVaR, which might be too conservative. S... | SP:661b75d2b9213295a69ab5f524c6983c67e783ec |
Distributional Reinforcement Learning for Risk-Sensitive Policies | 1 INTRODUCTION . In standard reinforcement learning ( RL ) ( Sutton & Barto , 2018 ) , one seeks to learn a policy that maximizes an objective , usually the expected total discounted rewards or the long-term average rewards . In stochastic domains , especially when the level of uncertainty involved is high , maximizing... | This paper is about risk-sensitive RL based on the CVaR risk measure. This paper is mainly based on the work presented in Dabney et al. in 2018 which is about distributional RL for a family of risk measures which includes CVaR as well. The main motivation for this work was the point that the method presented in Dabney ... | SP:661b75d2b9213295a69ab5f524c6983c67e783ec |
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization | 1 INTRODUCTION . Poylak ’ s Heavy Ball method ( Polyak ( 1964 ) ) has been very popular in modern non-convex optimization and deep learning , and the stochastic version ( a.k.a . SGD with momentum ) has become the de facto algorithm for training neural nets . Many empirical results show that the algorithm is better tha... | The authors analysed the dynamics heavy ball momentum in non-convex optimization settings (namely the phase retrieval and the cubic-regularized problems). The results show that the algorithm leads to a faster convergence rate in comparison with gradient descent without inertial contributions. Although there are results... | SP:47a15159ddb23477577e4378f534181cb8537ee5 |
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization | 1 INTRODUCTION . Poylak ’ s Heavy Ball method ( Polyak ( 1964 ) ) has been very popular in modern non-convex optimization and deep learning , and the stochastic version ( a.k.a . SGD with momentum ) has become the de facto algorithm for training neural nets . Many empirical results show that the algorithm is better tha... | This paper analyzes Polyak momentum in the deterministic case for two simple but important non-convex problems: phase retrieval and finding the cubic-regularized newton step. It is shown that in both cases the problems posses a “benign region” in which the objective “looks” a bit convex. In the phase retrieval case, as... | SP:47a15159ddb23477577e4378f534181cb8537ee5 |
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs | 1 INTRODUCTION . Research in automated planning and control has produced powerful algorithms to solve for optimal , or near-optimal , decisions given accurate environment models . Examples include the classic valueand policy-iteration algorithms for tabular representations or more sophisticated symbolic variants for gr... | The authors present a nearest neighbour method for learning a model offline from the statistics of the given data set. Representations are provided from other off-policy deep RL methods. Value iteration is used on top of the model to learn the final policy. The algorithm is tested in several Atari games over two data s... | SP:3f293ac26fc0a4ce0f2a9845905d7f7601b172dc |
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs | 1 INTRODUCTION . Research in automated planning and control has produced powerful algorithms to solve for optimal , or near-optimal , decisions given accurate environment models . Examples include the classic valueand policy-iteration algorithms for tabular representations or more sophisticated symbolic variants for gr... | The authors propose to learn a non-parametric MDP model from batch data, which can be solved efficiently using discrete value iteration (by solving for the “core” states which are all the end-states in observed transitions) and which provides a Q-value defined over the full continuous space through a kNN lookup. There ... | SP:3f293ac26fc0a4ce0f2a9845905d7f7601b172dc |
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities | 1 INTRODUCTION . Using hypothetical experience simulated from an environment model can significantly improve sample efficiency of RL agents ( Ha & Schmidhuber , 2018 ; Holland et al. , 2018 ; Pan et al. , 2018 ; Janner et al. , 2019 ; van Hasselt et al. , 2019 ) . Dyna ( Sutton , 1991 ) is a classical MBRL architecture... | This paper investigates the search-control problem in Dyna-style reinforcement learning algorithms. They first provide a theoretical justification behind the error-based prioritization and propose a new sampling method based on gradient ascent of which optimization results are equivalent to samples drawn from the prior... | SP:528ad3f6625f1cf86ac5219fba6ee1a457b65240 |
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities | 1 INTRODUCTION . Using hypothetical experience simulated from an environment model can significantly improve sample efficiency of RL agents ( Ha & Schmidhuber , 2018 ; Holland et al. , 2018 ; Pan et al. , 2018 ; Janner et al. , 2019 ; van Hasselt et al. , 2019 ) . Dyna ( Sutton , 1991 ) is a classical MBRL architecture... | The paper proposes a new way of prioritization in experience replay and Dyna-style planning methods. In particular, it proposes to exploit a learned model to actively search for states with high expected errors. The states are then prioritized proportional to the expected errors. The authors motivate the approach by a ... | SP:528ad3f6625f1cf86ac5219fba6ee1a457b65240 |
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning | 1 INTRODUCTION . In RL , as an agent explores more of its environment and updates its policy and value function , the data distribution it uses for training changes . In deep RL , this non-stationarity is often not addressed explicitly . Typically , a single neural network model is initialised and continually updated d... | The paper deals with a relevant issue. The simplified supervised learning setting is a good way of looking at the issue of non-stationarity in isolation and it makes a compelling case that neural networks optimized by SGD can have generalization issues in settings where the data distribution changes over time, even aft... | SP:d2d5800a67dc1972370362f55665a8fe2f25f961 |
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning | 1 INTRODUCTION . In RL , as an agent explores more of its environment and updates its policy and value function , the data distribution it uses for training changes . In deep RL , this non-stationarity is often not addressed explicitly . Typically , a single neural network model is initialised and continually updated d... | This paper investigates an interesting problem that transient non-stationarity can affect the generalization of the neural network. This paper first conducts experiments on a supervised learning task to illustrate that transient non-stationarity can lead to degenerated performance on testing set. Then, the paper propos... | SP:d2d5800a67dc1972370362f55665a8fe2f25f961 |
Improving Local Effectiveness for Global Robustness Training | 1 INTRODUCTION . With the proliferation of deep neural networks ( DNN ) in areas including computer vision , natural language processing and speech recognition , there has been a growing concern over their safety . For example , Szegedy et al . ( 2013 ) demonstrated that naturally trained DNNs are in fact fragile . By ... | The paper proposes a new adversarial training scheme, LEAP, to obtain models robust against $\ell_\infty$-bounded adversarial examples. The loss used as the objective minimized during training involves both local and global (wrt the input space) properties of the network. Experiments suggest improved performance compar... | SP:cb123d2c425d00ef5dd94db8c8faa0210f72b57c |
Improving Local Effectiveness for Global Robustness Training | 1 INTRODUCTION . With the proliferation of deep neural networks ( DNN ) in areas including computer vision , natural language processing and speech recognition , there has been a growing concern over their safety . For example , Szegedy et al . ( 2013 ) demonstrated that naturally trained DNNs are in fact fragile . By ... | The authors developed a novel robust training algorithm LEAP to focus on the effective use of adversaries. The proposed method improves the model robustness at each local patch and combines these patches through a global term, achieves overall robustness. The authors showed by maximizing the use of adversaries, they ac... | SP:cb123d2c425d00ef5dd94db8c8faa0210f72b57c |
Self-supervised Adversarial Robustness for the Low-label, High-data Regime | 1 INTRODUCTION . As neural networks tackle challenges ranging from ranking content on the web ( Covington et al. , 2016 ) to autonomous driving ( Bojarski et al. , 2016 ) via medical diagnostics ( De Fauw et al. , 2018 ) , it has becomes increasingly important to ensure that deployed models are robust and generalize to... | This paper introduces a new algorithm for learning adversarially robust models in the semi-supervised setting, where a small amount of labeled data is available together with a sizeable unlabeled dataset. The proposed approach BYORL adapts an existing self-supervised learning method BYOL by introducing a new adversaria... | SP:f771887b75818f23da58a6d8f4cfa7f0ce640cc7 |
Self-supervised Adversarial Robustness for the Low-label, High-data Regime | 1 INTRODUCTION . As neural networks tackle challenges ranging from ranking content on the web ( Covington et al. , 2016 ) to autonomous driving ( Bojarski et al. , 2016 ) via medical diagnostics ( De Fauw et al. , 2018 ) , it has becomes increasingly important to ensure that deployed models are robust and generalize to... | The paper proposes a new self-supervised technique, Bootstrap Your Own Robust Latents (BYORL), based on an existing technique, BYOL. BYORL proposes to provide adversarially robust representations for low-label regimes. The paper claims that BYORL achieves state-of-the-art performance on CIFAR-10 even with data that is ... | SP:f771887b75818f23da58a6d8f4cfa7f0ce640cc7 |
Adversarial and Natural Perturbations for General Robustness | 1 INTRODUCTION . A large body of work in computer vision and machine learning research focuses on studying the robustness of neural networks against adversarial perturbations ( Kurakin et al. , 2016 ; Goodfellow et al. , 2014 ; Carlini & Wagner , 2017 ) . Various defense based methods have also been proposed against th... | In this paper, the authors evaluate the performance of classifiers trained and then later tested on both adversarially generated perturbations as well as more natural perturbations. By considering six different natural perturbations, they show empirically that natural perturbations can improve performance against clea... | SP:6cadee7608c7194037b971dcec3115929aa5e4fe |
Adversarial and Natural Perturbations for General Robustness | 1 INTRODUCTION . A large body of work in computer vision and machine learning research focuses on studying the robustness of neural networks against adversarial perturbations ( Kurakin et al. , 2016 ; Goodfellow et al. , 2014 ; Carlini & Wagner , 2017 ) . Various defense based methods have also been proposed against th... | This paper studies the effect of “robustification” (i.e., adversarial training or data augmentation) of models on the accuracy to seen and unseen perturbations. The authors propose a technique to “standardize” the robustification process across different perturbations. They evaluate their approach on several datasets, ... | SP:6cadee7608c7194037b971dcec3115929aa5e4fe |
Effective Abstract Reasoning with Dual-Contrast Network | EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK Tao Zhuo , Mohan Kankanhalli School of Computing , National University of Singapore zhuotao @ nus.edu.sg , mohan @ comp.nus.edu.sg ABSTRACT As a step towards improving the abstract reasoning capability of machines , we aim to solve Raven ’ s Progressive Matrices (... | The paper proposes a neural network based approach called Dual-Contrast Network (DCNet) to solve Raven’s Progressive Matrices (RPM). The approach consists of a rule contrast module that compares the latent rules between the unfilled (third) row/column and the filled (first and second) rows/columns, a choice contrast mo... | SP:1da35440d501c36cb1a46e431a3d9de0288933c8 |
Effective Abstract Reasoning with Dual-Contrast Network | EFFECTIVE ABSTRACT REASONING WITH DUAL-CONTRAST NETWORK Tao Zhuo , Mohan Kankanhalli School of Computing , National University of Singapore zhuotao @ nus.edu.sg , mohan @ comp.nus.edu.sg ABSTRACT As a step towards improving the abstract reasoning capability of machines , we aim to solve Raven ’ s Progressive Matrices (... | The paper proposes a new approach for abstract reasoning and explores it in the context of the RPM task. In contrast to other competing approaches, the authors seek to build into the model as few assumptions as possible to keep the model general and not specific to the specific problem or to particular annotations or s... | SP:1da35440d501c36cb1a46e431a3d9de0288933c8 |
Graph Edit Networks | 1 INTRODUCTION . Recent advances in graph representation learning have mostly focused on tasks of classification or regression , i.e . tasks with graph-structured input but numeric output ( Battaglia et al. , 2018 ; Kipf & Welling , 2016a ; Veličković et al. , 2018 ) . By contrast , few approaches to date can transfo... | Graph Editing is a prominent research area which overlaps a variety of fields in computer science. As a typical example, a series of graphs - each obtained from its predecessor - can naturally represent the evolution of a system over time. From this viewpoint, it would be natural to obtain a means of predicting how suc... | SP:8ade33116fbf73f44de1023349f61464bbae3ba2 |
Graph Edit Networks | 1 INTRODUCTION . Recent advances in graph representation learning have mostly focused on tasks of classification or regression , i.e . tasks with graph-structured input but numeric output ( Battaglia et al. , 2018 ; Kipf & Welling , 2016a ; Veličković et al. , 2018 ) . By contrast , few approaches to date can transfo... | This paper proposes a model that, given one graph, predicts a sequence of edits that transforms this graph to the next one in a sequence of evolving graphs. To this end, it proposes the graph edit network (GEN), which is a linear output layer that transforms node embeddings to a set of scores that are then used to dete... | SP:8ade33116fbf73f44de1023349f61464bbae3ba2 |
Novelty Detection via Robust Variational Autoencoding | 1 INTRODUCTION . Novelty detection refers to the task of detecting testing data points that deviate from the underlying structure of a given training dataset ( Chandola et al. , 2009 ; Pimentel et al. , 2014 ; Chalapathy & Chawla , 2019 ) . It finds crucial applications , in areas such as insurance and credit fraud ( Z... | The paper seeks to address the problem of novelty detection under the circumstance of having high corruptions in the training data. This is different from most previous work, which often assumes that training dataset is pure. To address this issue, a VAE-based approach is adopted in this paper, with several modificatio... | SP:77b9c09e04fa51dc104ced583ed8bbc270d73955 |
Novelty Detection via Robust Variational Autoencoding | 1 INTRODUCTION . Novelty detection refers to the task of detecting testing data points that deviate from the underlying structure of a given training dataset ( Chandola et al. , 2009 ; Pimentel et al. , 2014 ; Chalapathy & Chawla , 2019 ) . It finds crucial applications , in areas such as insurance and credit fraud ( Z... | This study proposes a novel method that can work well even the training data is corrupted by partial data from the unknown domain. Though it deals with the well-known problem called 'Noisy data/label', its approach is not the same thing as the previous works as it focuses on variational autoencoder on the task of novel... | SP:77b9c09e04fa51dc104ced583ed8bbc270d73955 |
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | 1 INTRODUCTION . A central task of learning is to find representations of the underlying data that efficiently and faithfully capture their structure . In the case of sequential data , one data point consists of a sequence of objects . This is a rich and non-homogeneous class of data and includes classical uni- or mult... | This paper introduces the free algebra, a classical mathematical concept as a generic tool to represent sequential data of arbitrary length. The proposed method has attractive theoretical property, such as preserving universality of static feature mapping, and convergence in the continuous setting. The author further p... | SP:26e8b2aa5dd4391723debdf5af6add655c7a3586 |
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | 1 INTRODUCTION . A central task of learning is to find representations of the underlying data that efficiently and faithfully capture their structure . In the case of sequential data , one data point consists of a sequence of objects . This is a rich and non-homogeneous class of data and includes classical uni- or mult... | This paper proposes to embed static feature maps into a larger linear space and shows that the proposed method achieves good performance on standard benchmarks. Detailed proofs and theoretical results are given in the appendices. The use of free algebras in ML seems novel and under-explored, although it is classical in... | SP:26e8b2aa5dd4391723debdf5af6add655c7a3586 |
Graph Information Bottleneck for Subgraph Recognition | 1 INTRODUCTION . Classifying the underlying labels or properties of graphs is a fundamental problem in deep graph learning with applications across many fields , such as biochemistry and social network analysis . However , real world graphs are likely to contain redundant even noisy information ( Franceschi et al. , 20... | I think this is a nice paper that successfully used information theoretic objective functions for graph representation learning. The authors leveraged the DONSKER approximation of mutual information for a global information bottleneck loss used on the input-space instead of learned latent-space. To help stabilise optim... | SP:f2ed2231574562ccb4154e423154e1095c58ffa8 |
Graph Information Bottleneck for Subgraph Recognition | 1 INTRODUCTION . Classifying the underlying labels or properties of graphs is a fundamental problem in deep graph learning with applications across many fields , such as biochemistry and social network analysis . However , real world graphs are likely to contain redundant even noisy information ( Franceschi et al. , 20... | The paper introduces the Graph Information Bottleneck (GIB) which aims to learn the most-informative compressed representation $Z$ given graph $G$ with associated label $Y$. Further, it defines GIB-Subgraph which aims to learn the compressed representation as the subgraph $G_{sub}$ which maximizes the mutual informatio... | SP:f2ed2231574562ccb4154e423154e1095c58ffa8 |
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization | 1 INTRODUCTION . In many recent machine learning optimization tasks , researchers not only focus on finding solutions with small prediction/generalization error but also concentrate on improving the interpretation of model by filtering out redundant parameters and achieving slimmer model architectures . One technique t... | The paper studies how to solve a class of group sparsity regularized minimization problems. In particular, a half-space stochastic projected gradient (HSPG) method is proposed, which is based on the Prox-SG and a new half-space step that promotes group sparsity. This step is to decompose the feasible space and then per... | SP:c64ba67ebf7b6abedb24f26cbd45e221cfd6b1d6 |
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization | 1 INTRODUCTION . In many recent machine learning optimization tasks , researchers not only focus on finding solutions with small prediction/generalization error but also concentrate on improving the interpretation of model by filtering out redundant parameters and achieving slimmer model architectures . One technique t... | This paper proposed a new algorithm for the group sparsity regularization problem. They claim most existing algorithms, though return solutions with low objective function value, only give dense solutions and cannot effectively ensure the desired structured sparsity. The new technique requires an initialization that is... | SP:c64ba67ebf7b6abedb24f26cbd45e221cfd6b1d6 |
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | 1 INTRODUCTION . Backpropagation ( Rumelhart et al. , 1986 ) has made a significant contribution to the success of deep learning as the core learning algorithm for SGD-based optimization . However , backpropagation is sequential in nature and supports only synchronous weight updates . Specifically , the limited concurr... | The paper proposes a method for decoupled training of neural networks called SEDONA. In the spirit of recent trends in greedy layer-wise and indirect training, SEDONA allows gradient information to flow either from the next layer as in backpropagation or from an auxiliary head, trying to make a prediction using the cur... | SP:e290ab27d2ef262375e49897784ecb70927a264c |
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | 1 INTRODUCTION . Backpropagation ( Rumelhart et al. , 1986 ) has made a significant contribution to the success of deep learning as the core learning algorithm for SGD-based optimization . However , backpropagation is sequential in nature and supports only synchronous weight updates . Specifically , the limited concurr... | This paper proposes a differentiable architecture search approach for splitting a deep network into locally-trained blocks to achieve training speedup. The approach achieves better performance than using backprop on small datasets (CIFAR10 and TinyImageNet), and comparable or slightly improved performance on ImageNet w... | SP:e290ab27d2ef262375e49897784ecb70927a264c |
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling | Neural networks have become the standard for image classification tasks . On one hand , convolutional neural networks ( CNNs ) achieve state-of-the-art performance by learning from a regular grid representation of images . On the other hand , graph neural networks ( GNNs ) have shown promise in learning image classific... | The paper introduces a new approach to leveraging graph neural networks for image tasks. While prior work has been based on constructing graphs using a super-pixel map using methods like SLIC, that generate super-pixels of all roughly the same size, the proposed method generates a scale-adaptive partition. It also intr... | SP:62b109418f4a6e8c755a0864e766448e37d2f9c3 |
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling | Neural networks have become the standard for image classification tasks . On one hand , convolutional neural networks ( CNNs ) achieve state-of-the-art performance by learning from a regular grid representation of images . On the other hand , graph neural networks ( GNNs ) have shown promise in learning image classific... | This paper introduces a wavelet-based superpixel algorithm and a spatially heterogeneous pooling. More specifically, they introduce an algorithm to compress images in the pixel domain and it leads non-uniformly distributed and multiscale superpixels. Furthermore, they introduced a spatially heterogeneous pooling method... | SP:62b109418f4a6e8c755a0864e766448e37d2f9c3 |
A Unified View on Graph Neural Networks as Graph Signal Denoising | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have shown great capacity in learning representations for graphstructured data and thus have facilitated many down-stream tasks such as node classification ( Kipf & Welling , 2016 ; Veličković et al. , 2017 ; Ying et al. , 2018a ; Klicpera et al. , 2018 ) and graph clas... | 1). The novelty and contribution are very limited. In literature, many papers have discussed the connection between different GNNs, typically, including aggregators and Updaters, such as discussed in “Deep Learning on Graphs: A Survey”. The submission only provides a kind of connection between GCN, GAT, PPPN and APPNP ... | SP:7a420981b1627e310e77f84be095f948c6af3e84 |
A Unified View on Graph Neural Networks as Graph Signal Denoising | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have shown great capacity in learning representations for graphstructured data and thus have facilitated many down-stream tasks such as node classification ( Kipf & Welling , 2016 ; Veličković et al. , 2017 ; Ying et al. , 2018a ; Klicpera et al. , 2018 ) and graph clas... | of the paper: In this paper, the authors make the following new argument: The aggregation processes of current popular GNN models such as GCN, GAT, PPNP, and APPNP can be treated as a graph denoising problem where the objective is to minimize a recovery error (a norm of noisy feature matrix, i.e. ||F-X||) plus a graph-... | SP:7a420981b1627e310e77f84be095f948c6af3e84 |
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains | 1 INTRODUCTION . Humans possess the incredible ability of being able to combine properties from multiple image distributions to create entirely new visual concepts . For example , Lake et al . ( 2015 ) discussed how humans can parse different object parts ( e.g. , wheels of a car , handle of a lawn mower ) and combine ... | This paper proposes a generative model as an extension of FineGAN that aims to learn a disentangled representation for image shape and appearance across different domains rather than "intra-domain" disentanglement. To this end, the authors adopt the prior that features that correspond to an object's appearance should p... | SP:7975585d96507de92ea59435396a8ed2fcc91008 |
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains | 1 INTRODUCTION . Humans possess the incredible ability of being able to combine properties from multiple image distributions to create entirely new visual concepts . For example , Lake et al . ( 2015 ) discussed how humans can parse different object parts ( e.g. , wheels of a car , handle of a lawn mower ) and combine ... | The submission describes a method to disentangle shape and appearance of images across two domains such that new images can be generated that have appearance and shape from either of these domains while still being visually convincing. Starting from an established method (FineGAN) to disentangle shape, appearance, back... | SP:7975585d96507de92ea59435396a8ed2fcc91008 |
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs | 1 INTRODUCTION . Natural language processing ( NLP ) encompasses a wide variety of applications such as summarization ( Kovaleva et al. , 2019 ) , information retrieval ( Zhan et al. , 2020 ) , and machine translation ( Tang et al. , 2018 ) , among others . Currently , the use of pre-trained language models has become ... | The authors conduct a study investigating how different language models incorporate semantic information in their respective learned representations. Investigating language models on their performance in concept-level tasks is motivated by the importance of the ability to organize and understand concepts in human inte... | SP:3fb778bf2a70b808044c4cd4be288a4d1a9a3465 |
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs | 1 INTRODUCTION . Natural language processing ( NLP ) encompasses a wide variety of applications such as summarization ( Kovaleva et al. , 2019 ) , information retrieval ( Zhan et al. , 2020 ) , and machine translation ( Tang et al. , 2018 ) , among others . Currently , the use of pre-trained language models has become ... | This work addresses the question about how pre-trained language models encode semantic information. It adapts the methodology proposed in Hewitt & Manning (2019) for syntax to semantics, using the WordNet structure instead of a syntactic structure of a sentence to encode distances among word representations. The paper ... | SP:3fb778bf2a70b808044c4cd4be288a4d1a9a3465 |
Certified Distributional Robustness via Smoothed Classifiers | 1 INTRODUCTION . Deep neural networks ( DNNs ) have been known to be vulnerable to adversarial example attacks : by feeding the DNN with slightly perturbed inputs , the attack alters the prediction output . The attack can be fatal in performance-critical systems such as autonomous vehicles or automated tumor diagnosis ... | This paper studies the problem of certified robustness in adversarial learning. In a nutshell, they apply the randomized smoothing technique to the distributional robustness certificate proposed by Sinha et al. (2018), thereby relaxing the smoothness assumption required therein so that the ReLU network can be applied. ... | SP:5eb3d197fb5005f876dac170b9a40717d965f66c |
Certified Distributional Robustness via Smoothed Classifiers | 1 INTRODUCTION . Deep neural networks ( DNNs ) have been known to be vulnerable to adversarial example attacks : by feeding the DNN with slightly perturbed inputs , the attack alters the prediction output . The attack can be fatal in performance-critical systems such as autonomous vehicles or automated tumor diagnosis ... | This paper proposes smoothing the classifier in the distributional robust learning framework by adding random noise to the input. The smoothed distributional robust framework is used to gain robustness against adversarial perturbations in settings where the classifier is originally non-smooth and then smoothed via the ... | SP:5eb3d197fb5005f876dac170b9a40717d965f66c |
Model-Based Visual Planning with Self-Supervised Functional Distances | 1 INTRODUCTION . Designing general-purpose robots that can perform a wide range of tasks remains an open problem in AI and robotics . Reinforcement learning ( RL ) represents a particularly promising tool for learning robotic behaviors when skills can be learned one at a time from user-defined reward functions . Howeve... | This paper proposes to learn functional distances to varying goals concurrently with a latent dynamics model from images. A network is trained to predict the Q-value of state action pairs for a sparse reward at the goal. This way the Q-function represents the shortest path distance to the goal. The paper also proposes ... | SP:a1cb44f75e6ce83ead86c295ca0a1c51e3a4f456 |
Model-Based Visual Planning with Self-Supervised Functional Distances | 1 INTRODUCTION . Designing general-purpose robots that can perform a wide range of tasks remains an open problem in AI and robotics . Reinforcement learning ( RL ) represents a particularly promising tool for learning robotic behaviors when skills can be learned one at a time from user-defined reward functions . Howeve... | The paper combines optimal control and reinforcement learning (RL) for the execution of robotic manipulation tasks with variable goals. The approach learns, on the one hand, an image-based predictive model using a deep neural network and, on the other hand, a distance cost function using Q-Learning. These model and cos... | SP:a1cb44f75e6ce83ead86c295ca0a1c51e3a4f456 |
Decentralized SGD with Asynchronous, Local and Quantized Updates | 1 INTRODUCTION . Several techniques have been recently explored for scaling the distributed training of machine learning models , such as communication-reduction , asynchronous updates , or decentralized execution . For background , consider the classical data-parallel distribution strategy for SGD ( Bottou , 2010 ) , ... | The paper proposes and analyses a distributed learning algorithm for training with Stochastic Gradient Descent a global model on a regular graph, that allows for local and asynchronous gradient updates. Nodes continuously update their local models $X^i$ by gradient descent, while they communicate with their peers (a pe... | SP:120a40819527a564d4d1f66b2d138f849e44ed26 |
Decentralized SGD with Asynchronous, Local and Quantized Updates | 1 INTRODUCTION . Several techniques have been recently explored for scaling the distributed training of machine learning models , such as communication-reduction , asynchronous updates , or decentralized execution . For background , consider the classical data-parallel distribution strategy for SGD ( Bottou , 2010 ) , ... | This paper combines the existing scaling techniques to reduce the communication cost of distributed SGD among a large number of computing nodes. These techniques include asynchronous, decentralized, or quantized communication. The authors prove that this combined algorithm converges to a local optimal point. In the exp... | SP:120a40819527a564d4d1f66b2d138f849e44ed26 |
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks | In Bayesian Deep Learning , distributions over the output of classification neural networks are approximated by first constructing a Gaussian distribution over the weights , then sampling from it to receive a distribution over the categorical output distribution . This is costly . We extend existing work to construct a... | The authors propose an approach called the Laplace Bridge to approximate predictive uncertainty in Bayesian neural networks. The approach is essentially based on first a change of variable, followed by a Laplace approximation. They provided a theoretical result for this approach, which essentially shows that for \alpha... | SP:1f5df656d73634346199738039c56f47b211d8c5 |
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks | In Bayesian Deep Learning , distributions over the output of classification neural networks are approximated by first constructing a Gaussian distribution over the weights , then sampling from it to receive a distribution over the categorical output distribution . This is costly . We extend existing work to construct a... | This article improves the efficiency of Bayesian neural networks (BNNs). It follows the sampling-free solutions within Bayesian deep learning (Wu et al., 2018; Haussmann et al., 2019, etc.). The difference is the proposed Laplace Bridge that approximates the full distribution over the softmax outputs of a neural netwo... | SP:1f5df656d73634346199738039c56f47b211d8c5 |
A new accelerated gradient method inspired by continuous-time perspective | 1 Introduction . Optimization is a core component of statistic and machine learning problems . Recently , gradientbased algorithms are widely used in such optimization problems due to its simplicity and efficiency for large-scale situations . For solving convex optimization problem min x∈Rd F ( x ) , where F ( x ) is c... | This paper proposes an accelerated method that has a high-order truncation error $O(h^4)$ to the ordinary differential equation $\ddot{x} + \frac{3}{t}\dot{x} + f(x) = 0$ obtained from Nesterov's accelerated method by (Su et al., 2014), while Nesterov's method has $O(h^3)$ error. This implies that the iterates of the p... | SP:38070da400d31759e1d0d9e30eefb5b7f9f4d640 |
A new accelerated gradient method inspired by continuous-time perspective | 1 Introduction . Optimization is a core component of statistic and machine learning problems . Recently , gradientbased algorithms are widely used in such optimization problems due to its simplicity and efficiency for large-scale situations . For solving convex optimization problem min x∈Rd F ( x ) , where F ( x ) is c... | Review: This paper refines the the truncation error analysis for discretizing the ODE to obtain accelerated optimization method. The truncation results include higher order term. Built upon the analysis, the authors propose a new method which is claimed to be more stable for large step size and converges faster. Nume... | SP:38070da400d31759e1d0d9e30eefb5b7f9f4d640 |
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning | 1 INTRODUCTION . The key to a wider and successful application of DRL techniques in real scenarios is the ability to adapt to the surrounding environment by generalizing from training experiences . These solutions have to cope with the uncertainties of the operational environment , requiring a huge number of trials to ... | Aiming at exploiting the benefits of population based policy optimization and policy gradient, this paper proposes a novel framework that combines these two techniques. The authors claim that the previously proposed frameworks that combines evolutionary approaches and policy gradient approaches uses actor critic approa... | SP:05977d1cb6550b36bb633626b45f69652bca2ac5 |
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning | 1 INTRODUCTION . The key to a wider and successful application of DRL techniques in real scenarios is the ability to adapt to the surrounding environment by generalizing from training experiences . These solutions have to cope with the uncertainties of the operational environment , requiring a huge number of trials to ... | The paper introduces Supe-RL that intermingles off-policy reinforcement learning with periodic beam search operations. The method makes a greedy selection between the rl-solution that it had and the best produced by the beam search using Polyak update to update the incumbent rl-solution if the one suggested by beam sea... | SP:05977d1cb6550b36bb633626b45f69652bca2ac5 |
Transformer protein language models are unsupervised structure learners | 1 INTRODUCTION . Unsupervised modeling of protein contacts has an important role in computational protein design ( Russ et al. , 2020 ; Tian et al. , 2018 ; Blazejewski et al. , 2019 ) and is a central element of all current state-of-the-art structure prediction methods ( Wang et al. , 2017 ; Senior et al. , 2020 ; Yan... | In this paper, the authors show that transformer protein language models can learn protein contacts from the unsupervised language modelling objectives. They also show that the residue-residue contacts can be extracted by sparse logistic regression to learn coefficients on the attention heads. One of the advantages of ... | SP:a826495e7d92c3cd68a71fc4961c296fec0307ed |
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