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Classification-Based Anomaly Detection for General Data | 1 INTRODUCTION . Detecting anomalies in perceived data is a key ability for humans and for artificial intelligence . Humans often detect anomalies to give early indications of danger or to discover unique opportunities . Anomaly detection systems are being used by artificial intelligence to discover credit card fraud ,... | This paper proposes a novel approach to classification-based anomaly detection for general data. Classification-based anomaly detection uses auxiliary tasks (transformations) to train a model to extract useful features from the data. This approach is well-known in image data, where auxiliary tasks such as classificatio... | SP:5674e8decbf353c9e5590e5c85ee5b8397a5db08 |
Classification-Based Anomaly Detection for General Data | 1 INTRODUCTION . Detecting anomalies in perceived data is a key ability for humans and for artificial intelligence . Humans often detect anomalies to give early indications of danger or to discover unique opportunities . Anomaly detection systems are being used by artificial intelligence to discover credit card fraud ,... | Review: The paper proposes a technique for anomaly detection. It presents a novel method that unifies the current classification-based approaches to overcome generalization issues and outperforms the state of the art. This work also generalizes to non-image data by extending the transformation functions to include rand... | SP:5674e8decbf353c9e5590e5c85ee5b8397a5db08 |
Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation | 1 INTRODUCTION . Context based machine translation ( CBMT ) is a phrase-based machine translation ( PBMT ) approach proposed by Miller et al . ( 2006 ) . Unlike most PBMT approaches that rely on statistical occurrence of the phrases , CBMT works on the contextual occurrence of the phrases . CBMT uses bilingual dictiona... | This paper aims to combine a traditional CBMT system with an NMT system. The core idea of the paper is to use the output of the CBMT system as a second source to a multi-source NMT system. The first source of the system is CBMT, the second source is the original source and the output is the translation in the target la... | SP:e65bda143869e9a4d75b7e7ee893a2ed7b8e822a |
Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation | 1 INTRODUCTION . Context based machine translation ( CBMT ) is a phrase-based machine translation ( PBMT ) approach proposed by Miller et al . ( 2006 ) . Unlike most PBMT approaches that rely on statistical occurrence of the phrases , CBMT works on the contextual occurrence of the phrases . CBMT uses bilingual dictiona... | This paper presents a machine translation system based on a combination of a neural machine translation system (NMT) and a context-based machine translation (CBMT). The method is evaluated on a small parallel corpus application of English-Amharic translation. The idea is that in the small corpus setting, the CBMT can l... | SP:e65bda143869e9a4d75b7e7ee893a2ed7b8e822a |
Information Geometry of Orthogonal Initializations and Training | 1 INTRODUCTION . Deep neural networks ( DNN ) have shown tremendous success in computer vision problems , speech recognition , amortized probabilistic inference , and the modelling of neural data . Despite their performance , DNNs face obstacles in their practical application , which stem from both the excessive comput... | This paper analyses the training behavior of wide networks and argues orthogonal initialization helps the training. They suggest projections to the manifold of orthogonal weights during training and provide analysis. Their main result seems to be a bound on the eigen-values of the Fisher information matrix for wide net... | SP:7a47dd0e43f8e18913551cdb7207ad3333472e22 |
Information Geometry of Orthogonal Initializations and Training | 1 INTRODUCTION . Deep neural networks ( DNN ) have shown tremendous success in computer vision problems , speech recognition , amortized probabilistic inference , and the modelling of neural data . Despite their performance , DNNs face obstacles in their practical application , which stem from both the excessive comput... | This paper formulates a connection between the Fisher information matrix (FIM) and the spectral radius of the input-output Jacobian in neural networks. This results derive the eigenvalues' bound to theoretically study the convergence of several networks. Here the upper bound further improves the upper bound of FIM der... | SP:7a47dd0e43f8e18913551cdb7207ad3333472e22 |
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings | 1 INTRODUCTION Joint image-text representations find application in cross-domain tasks such as imageconditioned text generation ( captioning ; Mao et al. , 2015 ; Karpathy & Fei-Fei , 2017 ; Xu et al. , 2018 ) and text-conditioned image synthesis ( Reed et al. , 2016 ) . Yet , image and text distributions follow distin... | This paper addresses the problem of many-to-many cross domain mapping tasks (such as captioning or text-to-image synthesis). It proposes a double variational auto-encoder architecture mapping data to a factored latent representation with both shared and domain-specific components. The proposed model makes use of normal... | SP:22065b789e9ea434dcfae0443f24f1bbd95e116f |
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings | 1 INTRODUCTION Joint image-text representations find application in cross-domain tasks such as imageconditioned text generation ( captioning ; Mao et al. , 2015 ; Karpathy & Fei-Fei , 2017 ; Xu et al. , 2018 ) and text-conditioned image synthesis ( Reed et al. , 2016 ) . Yet , image and text distributions follow distin... | The paper introduces a variational model for text to image and image to text mappings. The novelty consists in separating the modeling of text and image latent representations on one hand and the modeling of a shared content representation on the other hand. Priors for text, image and shared representations are generat... | SP:22065b789e9ea434dcfae0443f24f1bbd95e116f |
Topology-Aware Pooling via Graph Attention | Pooling operations have shown to be effective on various tasks in computer vision and natural language processing . One challenge of performing pooling operations on graph data is the lack of locality that is not well-defined on graphs . Previous studies used global ranking methods to sample some of the important nodes... | This paper proposes a topology-aware pooling method on graph data, which explicitly encodes the topology information when computing ranking scores. More specifically, the proposed method uses an attention operator to compute similarity scores between each node and its neighborhood nodes, and then uses the average simil... | SP:be7c42be6523a9923bf4701c9854816d0d8d2494 |
Topology-Aware Pooling via Graph Attention | Pooling operations have shown to be effective on various tasks in computer vision and natural language processing . One challenge of performing pooling operations on graph data is the lack of locality that is not well-defined on graphs . Previous studies used global ranking methods to sample some of the important nodes... | This paper presented a new pooling method for learning graph-level embeddings. The key idea is to use the initial node attributes to compute all-pair attention scores for each node pair and then use these attention scores to formulate a new graph adjacency matrix beyond the original raw graph adjacency matrix. As the r... | SP:be7c42be6523a9923bf4701c9854816d0d8d2494 |
RGTI:Response generation via templates integration for End to End dialog | End-to-end models have achieved considerable success in task-oriented dialogue area , but suffer from the challenges of ( a ) poor semantic control , and ( b ) little interaction with auxiliary information . In this paper , we propose a novel yet simple end-to-end model for response generation via mixed templates , whi... | 1. Summary: The authors proposed a deep neural network-based model to generate responses fro task-oriented dialogue systems. The model mainly contains two parts, the first part is to retrieve relevant responses based on question and encode them into templates, the second part is a decoder to generate the response based... | SP:f5e27c7a2ae2bda209fb5befbc3ea366b4d71adc |
RGTI:Response generation via templates integration for End to End dialog | End-to-end models have achieved considerable success in task-oriented dialogue area , but suffer from the challenges of ( a ) poor semantic control , and ( b ) little interaction with auxiliary information . In this paper , we propose a novel yet simple end-to-end model for response generation via mixed templates , whi... | This paper describes a method to incorporate candidate templates to aid in response generation within an end-to-end dialog system. While the motivation and task setup is interesting, the paper is clearly unfinished. Most jarringly, Table 2 which should contain the main results comparing the proposed RGTI model to exist... | SP:f5e27c7a2ae2bda209fb5befbc3ea366b4d71adc |
Removing the Representation Error of GAN Image Priors Using the Deep Decoder | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) show promise as priors for solving imaging inverse problems such as inpainting , compressive sensing , super-resolution , and others . For example , they have been shown to perform as well as common sparsity based priors on compressed sensing tasks using 5-10x f... | This paper proposes to use a combination of a pretrained GAN and an untrained deep decoder as the image prior for image restoration problem. The combined model jointly infers the latent code for the trained GAN and the parameters in the untrained deep decoder. It also jointly infers the mixing coefficient alpha and bet... | SP:34824d19f70879da119b1ecd77d64b06ebca462d |
Removing the Representation Error of GAN Image Priors Using the Deep Decoder | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) show promise as priors for solving imaging inverse problems such as inpainting , compressive sensing , super-resolution , and others . For example , they have been shown to perform as well as common sparsity based priors on compressed sensing tasks using 5-10x f... | This paper presents a method for reducing the representation error generative convolutional neural networks by combining them with untrained deep decoder. The method is evaluated on compressive sensing and super-resolution, where a better performance than the isolated use of Deep Decoders and GAN priors. The main contr... | SP:34824d19f70879da119b1ecd77d64b06ebca462d |
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells | 1 INTRODUCTION . Unsupervised text encoding models such as Word2Vec ( Mikolov et al. , 2013 ) , Glove ( Pennington et al. , 2014 ) , ELMo ( Peters et al. , 2018 ) , and BERT ( Devlin et al. , 2018 ) have been effectively utilized in many Natural Language Processing ( NLP ) tasks . At their core they train models which ... | The paper introduces Space2Vec, a space representation learning model. The work is motivated by the biological grid cell’s multi-scale periodic representations and the success of representation learning of NLP. So, the key idea behind the model is two-fold. On one hand, utilize the position information and the context ... | SP:062377d8728ec1cc76ecd9a32deddaf1fcd58763 |
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells | 1 INTRODUCTION . Unsupervised text encoding models such as Word2Vec ( Mikolov et al. , 2013 ) , Glove ( Pennington et al. , 2014 ) , ELMo ( Peters et al. , 2018 ) , and BERT ( Devlin et al. , 2018 ) have been effectively utilized in many Natural Language Processing ( NLP ) tasks . At their core they train models which ... | This paper presents a new method called "Space2Vec" to compute spatial embeddings of a pixel in a spatial data. The primary motivation of Space2Vec is to integrate representations of different spatial scales which could potentially make the spatial representations more informative and meaningful as features. Space2Vec ... | SP:062377d8728ec1cc76ecd9a32deddaf1fcd58763 |
Adjustable Real-time Style Transfer | 1 INTRODUCTION . Style transfer is a long-standing problem in computer vision with the goal of synthesizing new images by combining the content of one image with the style of another ( Efros & Freeman , 2001 ; Hertzmann , 1998 ; Ashikhmin , 2001 ) . Recently , neural style transfer techniques ( Gatys et al. , 2015 ; 20... | The paper proposed a generative model for image style transfer in real time. In particular, comparing to the existing work, the proposed method is able to generate a series of transferred images instead of one, and more importantly, users can adjust different parameters without re-training the network to control over t... | SP:4535803bbaeba4ee21bd85c05ff7ecea4fdbfe10 |
Adjustable Real-time Style Transfer | 1 INTRODUCTION . Style transfer is a long-standing problem in computer vision with the goal of synthesizing new images by combining the content of one image with the style of another ( Efros & Freeman , 2001 ; Hertzmann , 1998 ; Ashikhmin , 2001 ) . Recently , neural style transfer techniques ( Gatys et al. , 2015 ; 20... | The paper presents an approach for style transfer with controlable parameters. The controllable parameters correspond to the weights associated to "style losses" or ordinary style transfer models (distance between gram matrices of generated vs style image at specific layers of a network). The authors propose to learn a... | SP:4535803bbaeba4ee21bd85c05ff7ecea4fdbfe10 |
Exploration Based Language Learning for Text-Based Games | 1 INTRODUCTION . Text-based games became popular in the mid 80s with the game series Zork ( Anderson & Galley , 1985 ) resulting in many different text-based games being produced and published ( Spaceman , 2019 ) . These games use a plain text description of the environment and the player has to interact with them by w... | This paper applies the Go-Explore algorithm to the domain of text-based games and shows significant performance gains on Textworld's Coin Collector and Cooking sets of games. Additionally, the authors evaluate 3 different paradigms for training agents on (1) single games, (2) jointly on multiple games, and (3) training... | SP:c578bd6652d1dcd0e280d587ffc973dddf3146c6 |
Exploration Based Language Learning for Text-Based Games | 1 INTRODUCTION . Text-based games became popular in the mid 80s with the game series Zork ( Anderson & Galley , 1985 ) resulting in many different text-based games being produced and published ( Spaceman , 2019 ) . These games use a plain text description of the environment and the player has to interact with them by w... | This paper considers the task of training an agent to play text-based computer games. One of the key challenges is the high-dimensional action space in these games, which poses a problem for many current methods. The authors propose to learn an LSTM-based decoder to output the action $a_t$ by greedily prediction one wo... | SP:c578bd6652d1dcd0e280d587ffc973dddf3146c6 |
Limitations for Learning from Point Clouds | In this paper we prove new universal approximation theorems for deep learning on point clouds that do not assume fixed cardinality . We do this by first generalizing the classical universal approximation theorem to general compact Hausdorff spaces and then applying this to the permutation-invariant architectures presen... | PointNet (Qi et al, 2017) and Deep sets (Zaheer et al, 2017) have allowed to use deep architectures that deal with point clouds as inputs, taking into account the invariance in the ordering of points. However, existing results on their approximation abilities are limited to fixed cardinalities. This paper removes the c... | SP:048d4b0525787b7a697c5608f0dd20ef84ebe339 |
Limitations for Learning from Point Clouds | In this paper we prove new universal approximation theorems for deep learning on point clouds that do not assume fixed cardinality . We do this by first generalizing the classical universal approximation theorem to general compact Hausdorff spaces and then applying this to the permutation-invariant architectures presen... | This work examines the fundamental properties of two popular architectures -- PointNet and DeepSets -- for processing point clouds (and other unordered sets). The authors provide a new universal approximation theorem on real-valued functions that doesn't require the assumption of a fixed cardinality of the input set. T... | SP:048d4b0525787b7a697c5608f0dd20ef84ebe339 |
Extreme Values are Accurate and Robust in Deep Networks | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) evolve very fast ever since AlexNet ( Krizhevsky & Hinton , 2012 ) makes a great breakthrough on ImageNet image classification challenge ( Deng et al. , 2009 ) in 2012 . Various network architectures have been proposed to further boost classification performance s... | This paper presents a SIFT-feature inspired modification to the standard convolutional neural network (CNN). Specifically the authors propose three innovations: (1) a differences of Gaussians (DoG) convolutional filter; (2) a symmetric ReLU activation function (referred to as a truncated ReLU; and (3) a projected norma... | SP:778ec97ea45befde6a8cba2e505f92c5706185e4 |
Extreme Values are Accurate and Robust in Deep Networks | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) evolve very fast ever since AlexNet ( Krizhevsky & Hinton , 2012 ) makes a great breakthrough on ImageNet image classification challenge ( Deng et al. , 2009 ) in 2012 . Various network architectures have been proposed to further boost classification performance s... | This paper proposes a network model named EVPNet, inspired by the idea scale-space extreme value from SIFT, to improve network robustness to adversarial pertubations over textures. To achieve better robustness, EVPNet separates outliers (non-robust) from robust examples by extenting DoG to parametric DoG, utilising tru... | SP:778ec97ea45befde6a8cba2e505f92c5706185e4 |
Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction | To flexibly and efficiently reason about temporal sequences , abstract representations that compactly represent the important information in the sequence are needed . One way of constructing such representations is by focusing on the important events in a sequence . In this paper , we propose a model that learns both t... | The paper introduces a model trained for video prediction hierarchically: a series of significant frames called “keyframes” in the paper are first predicted and then intermediate frames between keyframes couples are generated. The training criterion is maximum likelihood with a variational approximation. Experiments ar... | SP:8ead93266a4847d000548d8b05896b522d51e5f6 |
Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction | To flexibly and efficiently reason about temporal sequences , abstract representations that compactly represent the important information in the sequence are needed . One way of constructing such representations is by focusing on the important events in a sequence . In this paper , we propose a model that learns both t... | The authors address the problem of discovering and predicting with hierarchical structure in data sequences of relevance to planning. Starting with the kinds of data that have been used recently in video prediction, the authors aim at learning a sequence of keyframes (i.e., subsets of frames forming the overall sequenc... | SP:8ead93266a4847d000548d8b05896b522d51e5f6 |
Generative Latent Flow | 1 INTRODUCTION . Generative models have attracted much attention in the literature on deep learning . These models are used to formulate the distribution of complex data as a function of random noise passed through a network , so that rendering samples from the distribution is particularly easy . The most dominant gene... | The authors propose a model that combines a simple Auto-Encoder (AE) together with a Normalizing Flow (NF) model, such that to derive a generative model. In particular, the AE is used to learn a low-dimensional representation of the given data in a latent space. Then, a NF model learns under a maximum likelihood princi... | SP:55367291f235b256ca2f583722106e6507accd05 |
Generative Latent Flow | 1 INTRODUCTION . Generative models have attracted much attention in the literature on deep learning . These models are used to formulate the distribution of complex data as a function of random noise passed through a network , so that rendering samples from the distribution is particularly easy . The most dominant gene... | The paper proposes a new model combining an auto-encoder (AE) and a normalising flow (NF). The model, Generative Latent Flow (GLF), uses the AE to map the inputs to a latent space, which is then transformed using the NF. The approach is intuitively beneficial in that the AE can reduce the dimensionality of the inputs s... | SP:55367291f235b256ca2f583722106e6507accd05 |
Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax | 1 INTRODUCTION . Non-robustness of neural network models emerges as a pressing concern since they are observed to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2014 ) . Many attack methods have been developed to find imperceptible perturbations to fool the target classifiers ( Moos... | This paper studies classification problems via a reject option. A reject option could be useful in prediction problems to handle Out-of-distribution examples. The classification procedure studied in this paper builds on three components 1. An auto-encoder that obtains a latent low-dimensional representation of the data... | SP:0612639384f7b7766e8838d47a3ac973a6df0e1e |
Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax | 1 INTRODUCTION . Non-robustness of neural network models emerges as a pressing concern since they are observed to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2014 ) . Many attack methods have been developed to find imperceptible perturbations to fool the target classifiers ( Moos... | The paper proposes a scalable approach to train generative classifiers using information maximizing representation learning, with the motivation that generative classifiers could be more robust to adversarial attacks than discriminative classifiers. An off-the-shelf mutual information maximizer (MINE, DIM) is used to l... | SP:0612639384f7b7766e8838d47a3ac973a6df0e1e |
Ecological Reinforcement Learning | 1 INTRODUCTION . A central goal in current AI research , especially in reinforcement learning ( RL ) , is to develop algorithms that are general , in the sense that the same method can be used to train an effective model for a wide variety of tasks , problems , and domains . In RL , this means designing algorithms that... | This paper discusses the value of creating more challenging environments for training reinforcement learning agents. Specifically, the paper focuses on three characteristics of the environment that the paper claims are necessary for developing intelligent agents. The first of these properties is stochasticity in the en... | SP:ff58b8e7f4f0ae436627d7039f8c883a63f15101 |
Ecological Reinforcement Learning | 1 INTRODUCTION . A central goal in current AI research , especially in reinforcement learning ( RL ) , is to develop algorithms that are general , in the sense that the same method can be used to train an effective model for a wide variety of tasks , problems , and domains . In RL , this means designing algorithms that... | The authors study what they refer to as ecological reinforcement learning, defined as the interaction between properties of the environment and the reinforcement learning agent. They introduce environments with characteristics that reflect natural environments: non-episodic learning, uninformative reward signals, and n... | SP:ff58b8e7f4f0ae436627d7039f8c883a63f15101 |
Alleviating Privacy Attacks via Causal Learning | 1 INTRODUCTION . Machine learning algorithms , especially deep neural networks ( DNNs ) have found diverse applications in various fields such as healthcare ( Esteva et al. , 2019 ) , gaming ( Mnih et al. , 2013 ) , and finance ( Tsantekidis et al. , 2017 ; Fischer & Krauss , 2018 ) . However , a line of recent researc... | The authors consider a transfer learning problem where the source distribution is P(X,Y) while the target distribution is P*(X,Y) and classifier is trained on data from the source distribution. They also assume that the causal graph generating the data (X and Y) is identical while the conditional probabilities (mech... | SP:6260d6cfb07fe0981539d9a1e4a47d21479316ad |
Alleviating Privacy Attacks via Causal Learning | 1 INTRODUCTION . Machine learning algorithms , especially deep neural networks ( DNNs ) have found diverse applications in various fields such as healthcare ( Esteva et al. , 2019 ) , gaming ( Mnih et al. , 2013 ) , and finance ( Tsantekidis et al. , 2017 ; Fischer & Krauss , 2018 ) . However , a line of recent researc... | Overview: This paper discusses the risk of membership inference attacks that deep neural networks might face when used in a practical manner on real world datasets. Membership inference attacks can result in privacy breaches, a significant concern for many fields who might stand to benefit from using deep learning in a... | SP:6260d6cfb07fe0981539d9a1e4a47d21479316ad |
Learning Effective Exploration Strategies For Contextual Bandits | 1 INTRODUCTION . In a contextual bandit problem , an agent attempts to optimize its behavior over a sequence of rounds based on limited feedback ( Kaelbling , 1994 ; Auer , 2003 ; Langford & Zhang , 2008 ) . In each round , the agent chooses an action based on a context ( features ) for that round , and observes a rewa... | This paper proposes a meta-learning algorithm to solve the problem of exploration in a contextual bandit task using prior knowledge. This is analogous to how exploration strategies have been learned from past data in the meta-learning for RL (for example, [1]). Their algorithm simulates contextual bandit problems from ... | SP:bc0d62459bcb00a581f2273b5f4aabe3ead1d0c1 |
Learning Effective Exploration Strategies For Contextual Bandits | 1 INTRODUCTION . In a contextual bandit problem , an agent attempts to optimize its behavior over a sequence of rounds based on limited feedback ( Kaelbling , 1994 ; Auer , 2003 ; Langford & Zhang , 2008 ) . In each round , the agent chooses an action based on a context ( features ) for that round , and observes a rewa... | This paper introduced a meta-learning algorithm for the contextual bandit problem, MELEE, which learns an exploration policy based on simulated and synthetic contextual bandit tasks. The training is mainly divided into two steps. In step one, they proposed to train a policy optimizer, which maps features and action... | SP:bc0d62459bcb00a581f2273b5f4aabe3ead1d0c1 |
Differential Privacy in Adversarial Learning with Provable Robustness | 1 INTRODUCTION . The pervasiveness of machine learning exposes new vulnerabilities in software systems , in which deployed machine learning models can be used ( a ) to reveal sensitive information in private training data ( Fredrikson et al. , 2015 ) , and/or ( b ) to make the models misclassify , such as adversarial e... | This paper focus on providing both differential privacy and adversarial robustness to machine learning models. The authors propose an algorithm called differentially private adversarial learning (DPAL) to achieve such goal. DPAL consists two sub-models: (1) An auto-encoder to extract feature representation; and (2) A c... | SP:d26829f8a3a08c2935f10ab5871b847fd11c9887 |
Differential Privacy in Adversarial Learning with Provable Robustness | 1 INTRODUCTION . The pervasiveness of machine learning exposes new vulnerabilities in software systems , in which deployed machine learning models can be used ( a ) to reveal sensitive information in private training data ( Fredrikson et al. , 2015 ) , and/or ( b ) to make the models misclassify , such as adversarial e... | This paper propose an algorithm with DP preservation to train adversarially robust neural networks. To preserve DP, a single-layer linear autoencoder with shared weights is learned to extract features from training data, whose encoder is used to extract private features for the training and inference of a deeper networ... | SP:d26829f8a3a08c2935f10ab5871b847fd11c9887 |
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning | 1 INTRODUCTION . Q-learning ( Watkins , 1989 ) is one of the most popular reinforcement learning algorithms . One of the reasons for this widespread adoption is the simplicity of the update . On each step , the agent updates its action value estimates towards the observed reward and the estimated value of the maximal a... | This paper proposes a new Q learning algorithm framework: maxmin Q-learning, to address the overestimation bias issue of Q learning. The main contributions of this paper are three folds: 1) It provides an inspiring example on overestimation/underestimation of Q learning. 2) Generalize Q learning by a new maxmin Q-learn... | SP:e6e0533858e89d3cdf4265cb5a89ba6f4f9837bb |
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning | 1 INTRODUCTION . Q-learning ( Watkins , 1989 ) is one of the most popular reinforcement learning algorithms . One of the reasons for this widespread adoption is the simplicity of the update . On each step , the agent updates its action value estimates towards the observed reward and the estimated value of the maximal a... | The paper tackles the problem of bias in target Q-values when performing Q-learning. The paper proposes a technique for computing target Q-values, by first taking the min over an ensemble of learned Q-values and then taking the max over actions. The paper provides some theoretical properties of this technique: (1) th... | SP:e6e0533858e89d3cdf4265cb5a89ba6f4f9837bb |
Improved Generalization Bound of Permutation Invariant Deep Neural Networks | √ n ! where n is a number of permuting coordinates of data . Moreover , we prove that an approximation power of invariant deep neural networks can achieve an optimal rate , though the networks are restricted to be invariant . To achieve the results , we develop several new proof techniques such as correspondence with a... | This paper derives a generalization bound for permutation invariant networks. The main idea is to prove that the bound is inversely proportional to the square-root of the number of possible permutations to the input. The key result is Theorem 3 that bounds the covering number of a neural network (defined under an appro... | SP:62d218e9619a8a076aa2ef20f64bb26eb8516591 |
Improved Generalization Bound of Permutation Invariant Deep Neural Networks | √ n ! where n is a number of permuting coordinates of data . Moreover , we prove that an approximation power of invariant deep neural networks can achieve an optimal rate , though the networks are restricted to be invariant . To achieve the results , we develop several new proof techniques such as correspondence with a... | This paper presents a derivation of a generalization bound for neural networks designed specifically to deal with permutation invariant data (such as point clouds). The heart of the contribution is that the bound includes a 1/n! (i.e. 1 / (n-factorial)) factor to the major term, where n is the number of permutable ele... | SP:62d218e9619a8a076aa2ef20f64bb26eb8516591 |
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach | 1 Introduction . In training a reinforcement learning algorithm , an agent interacts with the environment , explores the ( possibly unknown ) state space , and learns a policy from the exploration sample data . In many cases , such samples are quite expensive to obtain ( e.g. , requires interactions with the physical e... | This paper proposes a representation learning algorithm for RL based on the Information Bottleneck (IB) principle. This formulation leads to the observed state X being mapped to a latent variable Z ~ P(Z | X), in such a way that the standard loss function in actor-critic RL methods is augmented with a term minimizing t... | SP:0b4c5468fbb7f0caaf2645c6d5c0a2159aec311d |
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach | 1 Introduction . In training a reinforcement learning algorithm , an agent interacts with the environment , explores the ( possibly unknown ) state space , and learns a policy from the exploration sample data . In many cases , such samples are quite expensive to obtain ( e.g. , requires interactions with the physical e... | In this paper, the authors proposed to utilize variational lower bound of mutual information for learning representations in Reinforcement learning. To optimize the proposed variational lower bound with a more flexible encoding network, the author proposed to utilize stein variational gradient descent (or amortized svg... | SP:0b4c5468fbb7f0caaf2645c6d5c0a2159aec311d |
Copy That! Editing Sequences by Copying Spans | 1 INTRODUCTION . Intelligent systems that assist users in achieving their goals have become a focus of recent research . One class of such systems are intelligent editors that identify and correct errors in documents while they are written . Such systems are usually built on the seq2seq ( Sutskever et al. , 2014 ) fram... | In this work, the authors tackle the problem of span-based copying in sequence-based neural models. In particular, they extend the standard copying techniques of (Vinyals et. al., Gulcehre et. al., etc.) which only allow for single-token copy actions. Their span-based copy mechanism allows for multiple tokens to be co... | SP:781c51554cfd04222ef6c6c92648d1824e054ae1 |
Copy That! Editing Sequences by Copying Spans | 1 INTRODUCTION . Intelligent systems that assist users in achieving their goals have become a focus of recent research . One class of such systems are intelligent editors that identify and correct errors in documents while they are written . Such systems are usually built on the seq2seq ( Sutskever et al. , 2014 ) fram... | This paper study the problem of editing sequences, such as natural language or code source, by copying large spans of the original sequence. A simple baseline solution to this problem is to learn a sequence to sequence neural network, which generates the edited sequence conditioned on the original one. This method can ... | SP:781c51554cfd04222ef6c6c92648d1824e054ae1 |
PDP: A General Neural Framework for Learning SAT Solvers | 1 INTRODUCTION . Constraint satisfaction problems ( CSP ) ( Kumar , 1992 ) and Boolean Satisfiability ( SAT ) , in particular , are the most fundamental NP-complete problems in Computer Science with a wide range of applications from verification to planning and scheduling . There have been huge efforts in Computer Scie... | This paper investigates the well-studied problem of solving satisfiability problems using deep learning approaches. In this setting, the authors propose a neural architecture inspired by message passing operations in deep probabilistic graphical models. Namely, the architecture takes as input a CNF formula represented ... | SP:d58f24e421f2022d85f95291fcf910262a76f590 |
PDP: A General Neural Framework for Learning SAT Solvers | 1 INTRODUCTION . Constraint satisfaction problems ( CSP ) ( Kumar , 1992 ) and Boolean Satisfiability ( SAT ) , in particular , are the most fundamental NP-complete problems in Computer Science with a wide range of applications from verification to planning and scheduling . There have been huge efforts in Computer Scie... | The authors develop an unsupervised method for solving SAT problems. The method consists of an energy-based loss function which is optimized by a three-stage architecture that performs propagation, decimation, and prediction (PDP). The authors show that on uniform random 4-SAT problems, their PDP system outperforms two... | SP:d58f24e421f2022d85f95291fcf910262a76f590 |
Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach | Motivated by applications to machine learning and imaging science , we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz continuous ; in particular , the loss functions encountered by the optimizer could exhibit gradient singularities or be singular themselves . Dra... | The paper establishes optimal regret bounds of the order O(\sqrt{T}) for Follow The Regularised Leader (FTRL) and Online Mirror Descent (OMD) for convex loss functions and potentials (a.k.a. Riemannian regularizers) that are, respectively, Lipschitz continuous and strongly convex with respect to a given Riemannian metr... | SP:893cf4309c06b75e6891831e684f59e4806d35b3 |
Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach | Motivated by applications to machine learning and imaging science , we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz continuous ; in particular , the loss functions encountered by the optimizer could exhibit gradient singularities or be singular themselves . Dra... | This paper investigates online and stochastic convex optimization problems in which the objective function is not Lipschitz continuous. The originality of this study lies in the use of Riemannian geometry. Specifically, the standard condition of Lipschitz continuity is replaced with a more general condition involving R... | SP:893cf4309c06b75e6891831e684f59e4806d35b3 |
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality | Granger causality is a widely-used criterion for analyzing interactions in largescale networks . As most physical interactions are inherently nonlinear , we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measureme... | In this paper the authors propose using Statistical Recurrent Units to predict the network for Granger causality. They motivate this choice by the high representation power of SRUs for multivariate time series, the good performance they usually enjoy and as a way to alleviate the vanishing gradient problem. More import... | SP:467735fd49561cd06342bb38a921e541553c6633 |
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality | Granger causality is a widely-used criterion for analyzing interactions in largescale networks . As most physical interactions are inherently nonlinear , we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measureme... | the paper attempts to infer Granger causality between nonlinearly interacting stochastic processes from their time series measurements. instead of using MLP/LSTM etc to to model time series measurement, the paper proposed to use component-wise time series prediction model with Statistical Recurrent Units to model the m... | SP:467735fd49561cd06342bb38a921e541553c6633 |
Situating Sentence Embedders with Nearest Neighbor Overlap | 1 INTRODUCTION . Continuous embeddings—of words and of larger linguistic units—are now ubiquitious in NLP . The success of self-supervised pretraining methods that deliver embeddings from raw corpora has led to a proliferation of embedding methods , with an eye toward “ universality ” across NLP tasks . Our focus here ... | The paper proposes N2O, a tool for probing the similarity among sentence embedders. Given two sentence embedders, N2O measures the amount of overlap of the k-nearest neighbor sets reported by the two embedders, averaged over a sample of probing queries. Cosine similarity is used as the similarity metric. The paper comp... | SP:8f949e0793cdee7336cf2c40803cb47202fef232 |
Situating Sentence Embedders with Nearest Neighbor Overlap | 1 INTRODUCTION . Continuous embeddings—of words and of larger linguistic units—are now ubiquitious in NLP . The success of self-supervised pretraining methods that deliver embeddings from raw corpora has led to a proliferation of embedding methods , with an eye toward “ universality ” across NLP tasks . Our focus here ... | The paper proposes a method to estimate the similarity of sentence embedders called N2O with the goal to better inform embedder choice in downstream applications. For two embedders A and B, N2O samples sentences called queries from a corpus, uses A and B to compute embeddings for each sentence, determines the k nearest... | SP:8f949e0793cdee7336cf2c40803cb47202fef232 |
Learning the Arrow of Time for Problems in Reinforcement Learning | 1 INTRODUCTION . The asymmetric progression of time has a profound effect on how we , as agents , perceive , process and manipulate our environment . Given a sequence of observations of our familiar surroundings ( e.g . as video frames ) , we possess the innate ability to predict whether the said observations are order... | This work proposes the h-potential, which is a solution to an objective that measures state-transition asymmetry in an MDP. Roughly speaking, in many situations some state transitions (s-->s’) are more probable than their converse (s’-->s), and if we have a function that assigns a higher value to a more probable transi... | SP:4e110cb77b848272f468030bfe05014d08d7b838 |
Learning the Arrow of Time for Problems in Reinforcement Learning | 1 INTRODUCTION . The asymmetric progression of time has a profound effect on how we , as agents , perceive , process and manipulate our environment . Given a sequence of observations of our familiar surroundings ( e.g . as video frames ) , we possess the innate ability to predict whether the said observations are order... | This paper proposes that we learn the “arrow of time” for an MDP: that is, a function (called the h-potential) that tends to increase as the MDP steps forward. Such an arrow should automatically capture notions such as irreversibility, and so can be used to define a measure of reachability, which previous work has show... | SP:4e110cb77b848272f468030bfe05014d08d7b838 |
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions | 1 INTRODUCTION . Evolutionary Strategies ( ES ) ( 1 ; 2 ; 3 ) are a black-box optimization technique , that estimate the gradient of some objective function with respect to the parameters by evaluating parameter perturbations in random directions . The benefits of using ES in Reinforcement Learning ( RL ) were exhibite... | This paper addresses the issue of noisy gradient estimation in a type of evolution strategies popularized by the open AI's reinforcement learning paper. It is a follow-up paper of reference [14], and try to analyze the optimality of the gradient estimation. The goal of the paper is well stated and well motivated. The p... | SP:ce211e46a1eac8bd3e35ccc30621bfdd53ba9a82 |
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions | 1 INTRODUCTION . Evolutionary Strategies ( ES ) ( 1 ; 2 ; 3 ) are a black-box optimization technique , that estimate the gradient of some objective function with respect to the parameters by evaluating parameter perturbations in random directions . The benefits of using ES in Reinforcement Learning ( RL ) were exhibite... | This paper provides a new type of gradient estimator that combines an Evolutionary Strategies (ES) style estimate (using function evaluations at perturbed parameters) along with surrogate gradient estimates (gradient estimates that may be biased and/or high variance). The estimator involves computing antithetic ES esti... | SP:ce211e46a1eac8bd3e35ccc30621bfdd53ba9a82 |
Where is the Information in a Deep Network? | 1 INTRODUCTION . At the end of training a deep neural network , all that is left of past experience is a set of values stored in its weights . So , studying what “ information ” they contain seems like a natural starting point to understand how deep networks learn . But how is the information in a deep neural network e... | This paper presents a theoretical account of information encoded within deep neural networks subject to information theoretic measures. In contrast to other efforts that examine information encoded in weights, this work emphasizes the effective information in the activations. This characterization is further related to... | SP:9da6cd132a934387f69fe759dbe5b1d2853242c5 |
Where is the Information in a Deep Network? | 1 INTRODUCTION . At the end of training a deep neural network , all that is left of past experience is a set of values stored in its weights . So , studying what “ information ” they contain seems like a natural starting point to understand how deep networks learn . But how is the information in a deep neural network e... | The paper deals with where the information is in a deep network and how information is propagated when new data points are observed. The authors measure information in the weights of a DNN as the trade-off between network accuracy and weight complexity. They bring out the relationships between Shannon MI and Fisher Inf... | SP:9da6cd132a934387f69fe759dbe5b1d2853242c5 |
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs | 1 INTRODUCTION . Deep architectures are generally trained by minimizing a non-convex loss function via underlying optimization algorithm such as stochastic gradient descent or its variants . It takes a fairly large amount of time to find the best suited optimization algorithm and its optimal hyperparameters ( such as l... | The paper considers the problem of automated adaptation of learning rate during (deep) neural network training. The use cases described are standard and adversarial training for image classification. Given the wide use of DNNs in computer vision (and other areas), learning rate tuning is clearly an important problem an... | SP:58b49ce9f05350745bc62b1ed2cb116fa07bb7d9 |
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs | 1 INTRODUCTION . Deep architectures are generally trained by minimizing a non-convex loss function via underlying optimization algorithm such as stochastic gradient descent or its variants . It takes a fairly large amount of time to find the best suited optimization algorithm and its optimal hyperparameters ( such as l... | This paper proposes an algorithm for automatically tuning the learning rate of SGD while training deep neural networks. The proposed learning rate tuning algorithm is a finite state machine and consists of two phases: the first phase finds the largest learning rate that the network can begin training with for p = 10 ep... | SP:58b49ce9f05350745bc62b1ed2cb116fa07bb7d9 |
TSInsight: A local-global attribution framework for interpretability in time-series data | 1 INTRODUCTION . Deep learning models have been at the forefront of technology in a range of different domains including image classification ( Krizhevsky et al. , 2012 ) , object detection ( Girshick , 2015 ) , speech recognition ( Dahl et al. , 2010 ) , text recognition ( Breuel , 2008 ) , image captioning ( Karpathy... | The paper presents a new approach for improving the interpretability of deep learning methods used for time series. The is mainly concerned with classification tasks for time series. First, the classifier is learned in a usual way. Subsequently, a sparse auto-encoder is used that encodes the last layer of the classifie... | SP:9fbad6b7a8485b00a2b22a46dca0f672f624c501 |
TSInsight: A local-global attribution framework for interpretability in time-series data | 1 INTRODUCTION . Deep learning models have been at the forefront of technology in a range of different domains including image classification ( Krizhevsky et al. , 2012 ) , object detection ( Girshick , 2015 ) , speech recognition ( Dahl et al. , 2010 ) , text recognition ( Breuel , 2008 ) , image captioning ( Karpathy... | The aim of this work is to improve interpretability in time series prediction. To do so, they propose to use a relatively post-hoc procedure which learns a sparse representation informed by gradients of the prediction objective under a trained model. In particular, given a trained next-step classifier, they propose to ... | SP:9fbad6b7a8485b00a2b22a46dca0f672f624c501 |
AN ATTENTION-BASED DEEP NET FOR LEARNING TO RANK | 1 INTRODUCTION . Learning to rank applies supervised or semi-supervised machine learning to construct ranking models for information retrieval problems . In learning to rank , a query is given and a number of search results are to be ranked by their relevant importance given the query . Many problems in information ret... | In this paper, the authors propose to use attention to combine multiple input representations for both query and search results in the learning to rank task. When these representations are embeddings from differentiable functions, they can be jointly learned with the neural network which predicts rankings. A limited se... | SP:969c99a939e5b56335f08b0d5828fa5a28842db0 |
AN ATTENTION-BASED DEEP NET FOR LEARNING TO RANK | 1 INTRODUCTION . Learning to rank applies supervised or semi-supervised machine learning to construct ranking models for information retrieval problems . In learning to rank , a query is given and a number of search results are to be ranked by their relevant importance given the query . Many problems in information ret... | The paper proposed an attention-based deep neural network for implementing 'learning to rank' algorithm. Particularly, the proposed method implements a listwise approach which outputs the ranks for all search results given a query. The search results are claimed to be sorted by their degree of relevance or importance t... | SP:969c99a939e5b56335f08b0d5828fa5a28842db0 |
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights | 1 INTRODUCTION . Deep Neural Networks have made considerable progress in various tasks such as image classification ( LeCun et al . 1998 , Simonyan & Zisserman 2014 , Szegedy et al . 2015 ) , object detection ( Ren et al . 2015 , Liu et al . 2016 ) , and speech recognition ( Graves et al . 2013 , Amodei et al . 2016 ) ... | The paper proposes a hybrid weights representation method where the weights of the neural network is split into two portions: a major portion of ternary weights and a minor portion of weights that are represented with different number of bits. The two portions of weights are differentiated by using the previous unused ... | SP:ad01c6b1219ff781129a7985c073e72ba5967763 |
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights | 1 INTRODUCTION . Deep Neural Networks have made considerable progress in various tasks such as image classification ( LeCun et al . 1998 , Simonyan & Zisserman 2014 , Szegedy et al . 2015 ) , object detection ( Ren et al . 2015 , Liu et al . 2016 ) , and speech recognition ( Graves et al . 2013 , Amodei et al . 2016 ) ... | This paper is about quantization, and how to represent values as the finite number of states in a low bit width, using discretization. Particularly, they propose an approach to tackle the problems associated with previous ternarization which quantize weights to three values. Their approach is a hybrid weight representa... | SP:ad01c6b1219ff781129a7985c073e72ba5967763 |
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has revolutionized the fields of AI and machine learning over the last decade . The introduction of deep learning has achieved unprecedented success in solving many problems that were intractable in the field of RL , such as playing Atari games from pixels and perform... | This paper proposed a new adversarial attack method based on model-based RL. Unlike existing adversarial attack methods on deep RL, the authors first approximate the dynamics models and then generate the adversarial samples by minimizing the total distance of each state to the pre-defined target state (i.e. planning). ... | SP:8033aa140ced2ef797bb83036759dd73acca5623 |
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has revolutionized the fields of AI and machine learning over the last decade . The introduction of deep learning has achieved unprecedented success in solving many problems that were intractable in the field of RL , such as playing Atari games from pixels and perform... | This paper looks at a new framework for adversarial attacks on deep reinforcement learning agents under continuous action spaces. They propose a model based approach which adds noise to either the observation or actions of the agent to push the agent to predefined target states. They then report results against sever... | SP:8033aa140ced2ef797bb83036759dd73acca5623 |
GQ-Net: Training Quantization-Friendly Deep Networks | 1 INTRODUCTION . Neural network quantization is a technique to reduce the size of deep networks and to bypass computationally and energetically expensive floating-point arithmetic operations in favor of efficient integer arithmetic on quantized versions of model weights and activations . Network quantization has been t... | This work introduces GQ-Net, a novel technique that trains quantization friendly networks that facilitate for 4 bit weights and activations. This is achieved by introducing a loss function that consists of a linear combination of two components: one that aims to minimize the error of the network on the training labels ... | SP:bd4fee07f87a3b40b274d1cbbae3ac07f11cb48d |
GQ-Net: Training Quantization-Friendly Deep Networks | 1 INTRODUCTION . Neural network quantization is a technique to reduce the size of deep networks and to bypass computationally and energetically expensive floating-point arithmetic operations in favor of efficient integer arithmetic on quantized versions of model weights and activations . Network quantization has been t... | In this paper, the authors propose a framework towards 4-bit auantization of CNNs. Specifically, during training, the proposed method contains a full precision branch supervised by classification loss for accurate prediction and representation learning, as well as a parameterized quantization branch to approximate the ... | SP:bd4fee07f87a3b40b274d1cbbae3ac07f11cb48d |
Policy Optimization by Local Improvement through Search | 1 INTRODUCTION . Reinforcement learning ( RL ) has seen a great deal of success in recent years , from playing games ( Mnih et al. , 2015 ; Silver et al. , 2016 ) to robotic control ( Gu et al. , 2017 ; Singh et al. , 2019 ) . These successes showcase the power of learning from direct interactions with environments . H... | This paper proposes POLISH, an imitation learning algorithm that provides a balance between Behavioral Cloning (BC) and DAgger. The algorithm reduces the mismatch between the target policy and an expert policy on states obtained from starting at the target policy's state distribution and following the expert policy for... | SP:943f1c5c3c9ba6d861df1a89eb9420d1f54d5573 |
Policy Optimization by Local Improvement through Search | 1 INTRODUCTION . Reinforcement learning ( RL ) has seen a great deal of success in recent years , from playing games ( Mnih et al. , 2015 ; Silver et al. , 2016 ) to robotic control ( Gu et al. , 2017 ; Singh et al. , 2019 ) . These successes showcase the power of learning from direct interactions with environments . H... | This paper proposes POLISH, a reinforcement learning learning algorithm based on imitating partial trajectories produced by an MCTS procedure. The intuition behind this idea is that behavioral cloning suffers from distribution shift over time, and using MCTS allows imitation learning to be done on states closer to the ... | SP:943f1c5c3c9ba6d861df1a89eb9420d1f54d5573 |
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem | We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem ( TSP ) . We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively . A graph neural network ( GNN ) is trained to capture the local and global g... | In this paper, the authors introduce a new Monte Carlo Tree Search-based (MCTS) algorithm for computing approximate solutions to the Traveling Salesman Problem (TSP). Yet since the TSP is NP-complete, a learned heuristic is used to guide the search process. For this learned heuristic, the authors propose a Graph Neural... | SP:83c5fa9ad4b7e0de17e75d4575316e84ad21a5b5 |
A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem | We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem ( TSP ) . We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively . A graph neural network ( GNN ) is trained to capture the local and global g... | The paper proposes learning a TSP solver that incrementally constructs a tour by adding one city at a time to it using a graph neural network and MCTS. The problem is posed as a reinforcement learning problem, and the graph neural network parameters are trained to minimize the tour length on a training set of TSP insta... | SP:83c5fa9ad4b7e0de17e75d4575316e84ad21a5b5 |
Learning Generative Image Object Manipulations from Language Instructions | The use of adequate feature representations is essential for achieving high performance in high-level human cognitive tasks in computational modeling . Recent developments in deep convolutional and recurrent neural networks architectures enable learning powerful feature representations from both images and natural lang... | This paper proposes a model that takes an image and a sentence as input, where the sentence is an instruction to manipulate objects in the scene, and outputs another image which shows the scene after manipulation. The model is an integration of CNN, RNN, Relation Nets, and GAN. The results are mostly on synthetic data,... | SP:68afc80f1983d8ae90181ca41c8132c09d78983d |
Learning Generative Image Object Manipulations from Language Instructions | The use of adequate feature representations is essential for achieving high performance in high-level human cognitive tasks in computational modeling . Recent developments in deep convolutional and recurrent neural networks architectures enable learning powerful feature representations from both images and natural lang... | 1. The paper aims to train a model to move objects in an image using language. For instance, an image with a red cube and blue ball needs to be turned into an image with a red cube and red ball if asked to "replace the red cube with a blue ball". The task itself is interesting as it aims to modify system behavior thro... | SP:68afc80f1983d8ae90181ca41c8132c09d78983d |
Kernelized Wasserstein Natural Gradient | 1 INTRODUCTION . The success of machine learning algorithms relies on the quality of an underlying optimization method . Many of the current state-of-the-art methods rely on variants of Stochastic Gradient Descent ( SGD ) such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Hinton et al. , 2012 ) , and Adam ( Kingma and... | The authors propose an approximate of the natural gradient under Wasserstein metric when optimizing some cost function over a parametric family of probability distributions. The authors leverage the dual formulation and restrict the feasible space to a RKHS. The authors show a trade-off between accuracy and computation... | SP:2565120a71aa1a9ff3677a993bb3bbd8c2271273 |
Kernelized Wasserstein Natural Gradient | 1 INTRODUCTION . The success of machine learning algorithms relies on the quality of an underlying optimization method . Many of the current state-of-the-art methods rely on variants of Stochastic Gradient Descent ( SGD ) such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Hinton et al. , 2012 ) , and Adam ( Kingma and... | Natural gradient has been proven effective in many statistical learning algorithms. A well-known difficulty in using natural gradient is that it is tedious to compute the Fisher matrix (if one is using Fisher-Rao metric) and the Wasserstein information matrix (if one is using Wasserstein metric). It's important to be a... | SP:2565120a71aa1a9ff3677a993bb3bbd8c2271273 |
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING | 1 INTRODUCTION . Imitation learning ( IL ) also known as learning from demonstrations allows agents to imitate expert demonstrations to make optimal decisions without direct interactions with the environment . Especially , inverse reinforcement learning ( IRL ) ( Ng et al . ( 2000 ) ) recovers a reward function of an e... | In this work, a multi-agent imitation learning algorithm for extensive Markov Games is proposed. Compared to Markov Games (MGs), extensive Markov Games (eMGs) introduces indicator variables, which means whether agents will participate in the game at the specific time step or not, and player function, which is a probabi... | SP:e00ee9485f054034892f307e0ab2f8df1e2d8701 |
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING | 1 INTRODUCTION . Imitation learning ( IL ) also known as learning from demonstrations allows agents to imitate expert demonstrations to make optimal decisions without direct interactions with the environment . Especially , inverse reinforcement learning ( IRL ) ( Ng et al . ( 2000 ) ) recovers a reward function of an e... | The submission extends the MARL◦MAIR to the extensive Markov game case, where the decisions are made asynchronously. As a result, a stronger equilibrium SPE is becomes the target of the proposed method. To this end, the submission takes advantage of the previous game theory results, to formulate the problem, and trans... | SP:e00ee9485f054034892f307e0ab2f8df1e2d8701 |
Measuring and Improving the Use of Graph Information in Graph Neural Networks | 1 INTRODUCTION . Graphs are powerful data structures that allow us to easily express various relationships ( i.e. , edges ) between objects ( i.e. , nodes ) . In recent years , extensive studies have been conducted on GNNs for tasks such as node classification and link predication . GNNs utilize the relationship inform... | The authors study how neighbor information on graphs can be used in Graph Neural Networks. It proposes measures on whether the data in neighboring nodes are useful in terms of labels or features. It also provides a new Graph Neural Network algorithm that is a modification of attention-based models incorporating the der... | SP:ff4879a21fee38d85c20afbb9c7fcac541ee3714 |
Measuring and Improving the Use of Graph Information in Graph Neural Networks | 1 INTRODUCTION . Graphs are powerful data structures that allow us to easily express various relationships ( i.e. , edges ) between objects ( i.e. , nodes ) . In recent years , extensive studies have been conducted on GNNs for tasks such as node classification and link predication . GNNs utilize the relationship inform... | The paper proposes two graph smoothness metrics for measuring the usefulness of graph information. The feature smoothness indicates how much information can be gained by aggregating neighboring nodes while the label smoothness assesses the quality of this information. The authors show that Graph Neural Networks (GNNs) ... | SP:ff4879a21fee38d85c20afbb9c7fcac541ee3714 |
Under what circumstances do local codes emerge in feed-forward neural networks | 1 INTRODUCTION . With neural networks ( NNs ) being widely deployed in various tasks it is essential to understand how they work and what data is used to make their decisions . NNs used to be viewed as ‘ black boxes ’ , but recent results ( Nguyen et al. , 2016 ) have started to open that box . NNs came from the field ... | I have a lot of questions about the data used in the experiments. They are created according to the method explained in “Data design” (p.2). It is also summarized in the last paragraph of the first section as follows: ”there are 1/10 input bits that are always 1 for each class and these are the invariant bits, the 0s o... | SP:975ca0db2d36004b48911303fd7fd8b61e956774 |
Under what circumstances do local codes emerge in feed-forward neural networks | 1 INTRODUCTION . With neural networks ( NNs ) being widely deployed in various tasks it is essential to understand how they work and what data is used to make their decisions . NNs used to be viewed as ‘ black boxes ’ , but recent results ( Nguyen et al. , 2016 ) have started to open that box . NNs came from the field ... | This paper aims to study when hidden units provide local codes by analyzing the hidden units of trained fully connected classification networks under various architectures and regularizers. The main text primarily studies networks trained on a dataset where binary inputs are structured to represent 10 classes with eac... | SP:975ca0db2d36004b48911303fd7fd8b61e956774 |
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness | 1 INTRODUCTION . Recent studies on mode connectivity show that two independently trained deep neural network ( DNN ) models with the same architecture and loss function can be connected on their loss landscape using a high-accuracy/low-loss path characterized by a simple curve ( Garipov et al. , 2018 ; Gotmare et al. ,... | This paper studies leveraging mode connectivity to defend against different types of attacks, including backdoor attacks, adversarial examples, and error-injection attacks. They perform a comprehensive evaluation to show the benign test accuracy and attack success rate over the models in the connected path between pair... | SP:a77494ee26aff245e217b630d3212aeee3d4496c |
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness | 1 INTRODUCTION . Recent studies on mode connectivity show that two independently trained deep neural network ( DNN ) models with the same architecture and loss function can be connected on their loss landscape using a high-accuracy/low-loss path characterized by a simple curve ( Garipov et al. , 2018 ; Gotmare et al. ,... | This paper proposes an adversarial defense method based on mode connectivity. The goal of the method is to repair tampered networks using a limited number of clean data examples. The authors consider two types of adversarial attacks: backdoor attacks and error-injection attacks. The proposed method takes two potentiall... | SP:a77494ee26aff245e217b630d3212aeee3d4496c |
Learning Structured Communication for Multi-agent Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved remarkable success in solving single-agent sequential decision problems under interactive and complicated environments , such as games ( Mnih et al. , 2015 ; Silver et al. , 2016 ) and robotics ( Lillicrap et al. , 2016 ) . In many real world applications such... | The authors learn structured communication patterns between multiple RL agents. Their framework uses a Structured Communication Network Module and Communication-based Policy Module. These use a hierarchical decomposition of the multi-agent system and a graph neural network that operates over the resulting abstract agen... | SP:ac1536424c9e62fa3ea6c40507a90a720679b23d |
Learning Structured Communication for Multi-agent Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved remarkable success in solving single-agent sequential decision problems under interactive and complicated environments , such as games ( Mnih et al. , 2015 ; Silver et al. , 2016 ) and robotics ( Lillicrap et al. , 2016 ) . In many real world applications such... | This paper proposes a method of learning a hierarchical communication graph for improving collaborative multi-agent reinforcement learning, particularly with large numbers of agents. The method is compared to a suitable range of baseline approaches across two complex environments. The initial results presented seem pro... | SP:ac1536424c9e62fa3ea6c40507a90a720679b23d |
Siamese Attention Networks | 1 INTRODUCTION . Deep learning networks with attention operators have demonstrated great capabilities of solving challenging problems in various tasks such as computer vision ( Xu et al. , 2015 ; Lu et al. , 2016 ) , natural language processing ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ) , and network embedding ... | In this paper, the authors propose a new mechanism to perform the attention operators. The similarity between a key and a query is performed as the dot product between a trainable weight and the addition of the key and query. The proposed Siamese attention operator is much more efficient than prior attention methods i... | SP:b44bdec4ffc5f79048deedf805b2835067bca899 |
Siamese Attention Networks | 1 INTRODUCTION . Deep learning networks with attention operators have demonstrated great capabilities of solving challenging problems in various tasks such as computer vision ( Xu et al. , 2015 ; Lu et al. , 2016 ) , natural language processing ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ) , and network embedding ... | The authors introduce a novel self-attention operator for neural networks. Their self-attention operator computes similarity between elements a and b as (a+b)^Tw where w is a learned parameter and does not use the softmax operator. This leads to improvements in space and time complexity compared to regular self-attenti... | SP:b44bdec4ffc5f79048deedf805b2835067bca899 |
Smart Ternary Quantization | 1 INTRODUCTION . Deep Neural Networks ( DNN ) models have achieved tremendous attraction because of their success on a wide variety of tasks including computer vision , automatic speech recognition , natural language processing , and reinforcement learning ( Goodfellow et al. , 2016 ) . More specifically , in computer ... | This paper studies mixed-precision quantization in deep networks where each layer can be either binarized or ternarized. The authors propose an adaptive regularization function that can be pushed to either 2-bit or 3-bit through different parameterization, in order to automatically determine the precision of each laye... | SP:faa869ec6fa32409248e46b957223595524e88df |
Smart Ternary Quantization | 1 INTRODUCTION . Deep Neural Networks ( DNN ) models have achieved tremendous attraction because of their success on a wide variety of tasks including computer vision , automatic speech recognition , natural language processing , and reinforcement learning ( Goodfellow et al. , 2016 ) . More specifically , in computer ... | The paper discusses a generalization to low bit quantization and combines the approaches of binary and ternary quantization methods. Past methods such as Binary Connect and Binary Weights Network have shown that you can train a network efficiently with 1-bit quantization, and methods such as Ternary Weights Network dem... | SP:faa869ec6fa32409248e46b957223595524e88df |
Which Tasks Should Be Learned Together in Multi-task Learning? | 1 INTRODUCTION . Many applications , especially robotics and autonomous vehicles , are chiefly interested in using multi-task learning to reduce the inference time and computational complexity required to estimate many characteristics of visual input . For example , an autonomous vehicle may need to detect the location... | This paper focuses on how to partition a bunch of tasks in several groups and then it use multi-task learning to improve the performance. The paper makes an observation that multi-task relationships are not entirely correlated to transfer relationships and proposes a computational framework to optimize the assignment ... | SP:61e4186bf0f3ce2e595196285f5f19e45d67a0d8 |
Which Tasks Should Be Learned Together in Multi-task Learning? | 1 INTRODUCTION . Many applications , especially robotics and autonomous vehicles , are chiefly interested in using multi-task learning to reduce the inference time and computational complexity required to estimate many characteristics of visual input . For example , an autonomous vehicle may need to detect the location... | This paper works on the problem if training a set of networks to solve a set of tasks. The authors try to discover an optimal task split into the networks so that the test performances are maximized given a fixed testing resource budget. By default, this requires searching over the entire task combination space and is ... | SP:61e4186bf0f3ce2e595196285f5f19e45d67a0d8 |
Finite Depth and Width Corrections to the Neural Tangent Kernel | 1 INTRODUCTION . Modern neural networks are typically overparameterized : they have many more parameters than the size of the datasets on which they are trained . That some setting of parameters in such networks can interpolate the data is therefore not surprising . But it is a priori unexpected that not only can such ... | This paper studies the finite depth and width corrections to the neural tangent kernel (NTK) in fully-connected ReLU networks. It gives sharp upper and lower bounds on the variance of NTK(x, x), which reveals an exponential dependence on a quantity beta=d/n, where d is depth, and n is hidden width. This implies that wh... | SP:fd4bc8557b3fd87ae1682252a55de0940854a2e8 |
Finite Depth and Width Corrections to the Neural Tangent Kernel | 1 INTRODUCTION . Modern neural networks are typically overparameterized : they have many more parameters than the size of the datasets on which they are trained . That some setting of parameters in such networks can interpolate the data is therefore not surprising . But it is a priori unexpected that not only can such ... | The paper investigates a novel infinite width limit taking depth to infinite at the same time. This is beyond conventional theoretical studies for infinite width networks where depth is kept finite when the width is taken to be infinite. The main object that paper studies is the neural tangent kernel which is of great ... | SP:fd4bc8557b3fd87ae1682252a55de0940854a2e8 |
Composition-based Multi-Relational Graph Convolutional Networks | 1 INTRODUCTION . Graphs are one of the most expressive data-structures which have been used to model a variety of problems . Traditional neural network architectures like Convolutional Neural Networks ( Krizhevsky et al. , 2012 ) and Recurrent Neural Networks ( Hochreiter & Schmidhuber , 1997 ) are constrained to handl... | In this paper, the authors developed GCN on multi-relational graphs and proposed CompGCN. In comparison with existing multi-relational GCN, CompGCN leverages insights from knowledge graph embedding and learns representations of both nodes and relations, with the aim to alleviate the problem of over-parameterization. Mo... | SP:82afe0f6d661432c3124eb14e9a83699e251143d |
Composition-based Multi-Relational Graph Convolutional Networks | 1 INTRODUCTION . Graphs are one of the most expressive data-structures which have been used to model a variety of problems . Traditional neural network architectures like Convolutional Neural Networks ( Krizhevsky et al. , 2012 ) and Recurrent Neural Networks ( Hochreiter & Schmidhuber , 1997 ) are constrained to handl... | This paper proposes a graph convolutional network based model for joint embedding of nodes and relations in a multi-relational graph. The framework comprises of node/relation embedding, nonparametric compositional operation as in knowledge graph embedding, and finally convolution operation with direction specific weig... | SP:82afe0f6d661432c3124eb14e9a83699e251143d |
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