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Disentangling 3D Prototypical Networks for Few-Shot Concept Learning | 1 INTRODUCTION . Humans can learn new concepts from just one or a few samples . Consider the example in Figure 1 . Assuming there is a person who has no prior knowledge about blue and carrot , by showing this person an image of a blue carrot and telling him “ this is an carrot with blue color ” , the person can easily ... | This paper presents a modular network architecture for few-shot concept learning. The architecture consists of image-to-scene module that maps input RGBD images to 3D scene features and an object-centric disentangling auto-encoder that crops object features to generate shape and style codes, and finally a neural render... | SP:8f46cbd5fff3557fe870cebf1ba231309fceab14 |
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning | 1 INTRODUCTION . Deep convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016b ; Huang et al. , 2017 ) have seen tremendous successes in a wide range of application fields , especially in visual recognition . However , the powerful learning ability of CNNs depends on a large amount of manua... | This paper addresses the problem of few-shot classification by incorporating self-supervised learning into the standard episode-based meta learning. Specifically, it adopts the pretext task of rotation prediction into the episode design. For each sampled episode, additional episodes are constructed by using rotated exa... | SP:c812cd7dae5afa5e60ba309053fec184cdc4cad2 |
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning | 1 INTRODUCTION . Deep convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016b ; Huang et al. , 2017 ) have seen tremendous successes in a wide range of application fields , especially in visual recognition . However , the powerful learning ability of CNNs depends on a large amount of manua... | This paper presents a method for combining self-supervised learning (SSL) (in the form of predicting the rotation applied to an image) with few-shot learning (FSL) in the domain of image classification. Compared to prior work, this paper introduces -- (i) a consistency loss which ensures FSL episodes with different rot... | SP:c812cd7dae5afa5e60ba309053fec184cdc4cad2 |
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning | 1 INTRODUCTION . Deep convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016b ; Huang et al. , 2017 ) have seen tremendous successes in a wide range of application fields , especially in visual recognition . However , the powerful learning ability of CNNs depends on a large amount of manua... | This paper solves the problem of few-shot learning. The recent success of SSL and FSL proves that they can handle situations that few label data are provided. Motivated by this, the author proposed a novel framework IEPT that seamlessly integrates self-supervised learning methods to few-shot learning. Unlike other triv... | SP:c812cd7dae5afa5e60ba309053fec184cdc4cad2 |
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation | 1 INTRODUCTION . Self-supervised pre-training of text representations ( Peters et al. , 2018 ; Radford et al. , 2018 ) has achieved tremendous success in natural language processing applications . Inspired by BERT ( Devlin et al. , 2019 ) , recent works attempt to leverage sequence-to-sequence model pre-training for Ne... | This submission proposes a joint training of self-supervised training and supervised training for neural machine translation (NMT), especially for the rich-resource language datasets. The method proposed, F$_2$-XEnDec, exploits the "crossover" operation of the monolingual sentences and bilingual data pairs in the encod... | SP:7ed79cb7d0bb12d3d782b5a79f07137563531484 |
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation | 1 INTRODUCTION . Self-supervised pre-training of text representations ( Peters et al. , 2018 ; Radford et al. , 2018 ) has achieved tremendous success in natural language processing applications . Inspired by BERT ( Devlin et al. , 2019 ) , recent works attempt to leverage sequence-to-sequence model pre-training for Ne... | This paper introduces a new approach to semi-supervised training of neural machine translation. During training, the traditional supervised loss is complemented with two auxiliary losses: a denoising autoencoder and what the authors call cross-breeding: shuffling together the source side of a sentence pair with an unre... | SP:7ed79cb7d0bb12d3d782b5a79f07137563531484 |
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation | 1 INTRODUCTION . Self-supervised pre-training of text representations ( Peters et al. , 2018 ; Radford et al. , 2018 ) has achieved tremendous success in natural language processing applications . Inspired by BERT ( Devlin et al. , 2019 ) , recent works attempt to leverage sequence-to-sequence model pre-training for Ne... | This paper proposes a joint training strategy that combines supervised learning on parallel data and self-supervised learning on monolingual data for NMT. The monolingual sentences are corrupted with word order shuffling and masking. And a cross encoder-decoder is introduced to fuse the parallel source-side sentence an... | SP:7ed79cb7d0bb12d3d782b5a79f07137563531484 |
Differentiable Weighted Finite-State Transducers | 1 INTRODUCTION . Weighted finite-state transducers ( WFSTs ) are a commonly used tool in speech and language processing ( Knight & May , 2009 ; Mohri et al. , 2002 ) . They are most frequently used to combine predictions from multiple already trained models . In speech recognition , for example , WFSTs are used to comb... | This is a well-written paper describing the incorporation of WFSTs into an "auto-diff" framework for Deep Learning. The central point is that by providing partial derivatives, in addition to the standard forward scores, to the major WFST operations, one can embed those WFST operations into a gradient-based deep learnin... | SP:f09077a0cb9dfb47788db6c18a0ec92c18d608b9 |
Differentiable Weighted Finite-State Transducers | 1 INTRODUCTION . Weighted finite-state transducers ( WFSTs ) are a commonly used tool in speech and language processing ( Knight & May , 2009 ; Mohri et al. , 2002 ) . They are most frequently used to combine predictions from multiple already trained models . In speech recognition , for example , WFSTs are used to comb... | This paper presents how weighted finite-state transducers (WFST) and a few common operations performed on them can be integrated in a differentiable model, and therefore contribute to the training of complete systems. The authors propose a few case studies, mainly in language applications, where the WFSTs are used to c... | SP:f09077a0cb9dfb47788db6c18a0ec92c18d608b9 |
Differentiable Weighted Finite-State Transducers | 1 INTRODUCTION . Weighted finite-state transducers ( WFSTs ) are a commonly used tool in speech and language processing ( Knight & May , 2009 ; Mohri et al. , 2002 ) . They are most frequently used to combine predictions from multiple already trained models . In speech recognition , for example , WFSTs are used to comb... | The authors introduce a library for differential weighted finite-state transducers. WFST are commonly used in speech or handwriting recognition systems but are generally not trained jointly with the deep neural networks components such as ConvNN. This is not due to theoretical limitation of WFST but rather to a lack o... | SP:f09077a0cb9dfb47788db6c18a0ec92c18d608b9 |
BeBold: Exploration Beyond the Boundary of Explored Regions | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has experienced significant progress over the last several years , with impressive performance in games like Atari ( Mnih et al. , 2015 ; Badia et al. , 2020a ) , StarCraft ( Vinyals et al. , 2019 ) and Chess ( Silver et al. , 2016 ; 2017 ; 2018 ) . However , most wor... | This paper is a presentation of BeBold, a new method using an intrinsic reward for exploration, meant for procedurally generated, episodic environments. The method includes two major components: the first being intrinsically rewarding the agent for entering states that are less visited than the current state and the se... | SP:cb17cc8e64068c1b5294af47cc07ccc3ebcada5b |
BeBold: Exploration Beyond the Boundary of Explored Regions | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has experienced significant progress over the last several years , with impressive performance in games like Atari ( Mnih et al. , 2015 ; Badia et al. , 2020a ) , StarCraft ( Vinyals et al. , 2019 ) and Chess ( Silver et al. , 2016 ; 2017 ; 2018 ) . However , most wor... | This paper proposes BeBold, a new definition of intrinsic reward to guide exploration in sparse reward problems. This intrinsic reward combines the ideas behind count-based approaches and state-diff approaches. They demonstrate the success of BeBold by comparing their algorithm to a set of state-of-the-art exploration ... | SP:cb17cc8e64068c1b5294af47cc07ccc3ebcada5b |
BeBold: Exploration Beyond the Boundary of Explored Regions | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has experienced significant progress over the last several years , with impressive performance in games like Atari ( Mnih et al. , 2015 ; Badia et al. , 2020a ) , StarCraft ( Vinyals et al. , 2019 ) and Chess ( Silver et al. , 2016 ; 2017 ; 2018 ) . However , most wor... | The authors propose a novel intrinsic reward based on the difference of inverse visitation counts for consecutive states. This reward encourages the agent to explore beyond the boundary of already explored regions. Using a few simple examples, they show that the proposed intrinsic reward mitigates the problems of detac... | SP:cb17cc8e64068c1b5294af47cc07ccc3ebcada5b |
How Benign is Benign Overfitting ? | 1 INTRODUCTION . Modern machine learning methods achieve a very high accuracy on wide range of tasks , e.g . in computer vision , natural language processing etc . However , especially in vision tasks , they have been shown to be highly vulnerable to small adversarial perturbations that are imperceptible to the human e... | The main contribution of the paper is to study the connection between adversarial robustness, on the one hand, and label noise & data representation on the other hand. Here, an algorithm is said to be robust if for every training example xi with label yi, one cannot find an instance x within a small distance of xi that... | SP:c95614e6cad71a73222413ff12a67e96ec487e40 |
How Benign is Benign Overfitting ? | 1 INTRODUCTION . Modern machine learning methods achieve a very high accuracy on wide range of tasks , e.g . in computer vision , natural language processing etc . However , especially in vision tasks , they have been shown to be highly vulnerable to small adversarial perturbations that are imperceptible to the human e... | The goal of the paper is to investigate both theoretically and empirically the reasons of vulnerability of overparameterized classifiers obtained by the so called “benign overfitting”. More precisely, two causes of adversarial vulnerability are underlined: label noise memorization and sub-optimal representation learnin... | SP:c95614e6cad71a73222413ff12a67e96ec487e40 |
How Benign is Benign Overfitting ? | 1 INTRODUCTION . Modern machine learning methods achieve a very high accuracy on wide range of tasks , e.g . in computer vision , natural language processing etc . However , especially in vision tasks , they have been shown to be highly vulnerable to small adversarial perturbations that are imperceptible to the human e... | The generalization ability of networks with zero training error has been heavily studied. This paper extends beyond generalization to test sets to study the network's robustness to adversarial examples. The paper provides two theoretical contributions demonstrating that a very low training error can indicate poor rob... | SP:c95614e6cad71a73222413ff12a67e96ec487e40 |
Optimal allocation of data across training tasks in meta-learning | Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones . They are tested on benchmarks with a fixed number of data-points for each training task , and this number is usually arbitrary , for example , 5 instances per class in few-shot classification . It is unknown how the per... | In most popular meta-learning approaches, there are usually a pre-defined number of data per task. For example, 1-shot or 5-shot learning. It has shown as the number of data increases in such methods, model performance improves. In this paper, they try to analyze the effect of having different number of tasks with diff... | SP:2dd943d37d914575aa6ac4f3948a2d5d9f53a8d9 |
Optimal allocation of data across training tasks in meta-learning | Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones . They are tested on benchmarks with a fixed number of data-points for each training task , and this number is usually arbitrary , for example , 5 instances per class in few-shot classification . It is unknown how the per... | The authors study the problem of finding the optimal allocation of labels across training tasks given a fixed budget. The authors mainly want to answer the question that "if the total number of labels across training tasks is limited, it is better to have a large number of tasks with very small data in each or a relat... | SP:2dd943d37d914575aa6ac4f3948a2d5d9f53a8d9 |
Optimal allocation of data across training tasks in meta-learning | Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones . They are tested on benchmarks with a fixed number of data-points for each training task , and this number is usually arbitrary , for example , 5 instances per class in few-shot classification . It is unknown how the per... | This paper proposes a data allocation scheme for meta-learning. The authors argue that it is important to consider the number of total tasks versus the number of datapoints per task given a fixed budget of the total number of datapoints since labeling is expensive for large datasets. The paper presents an algorithm tha... | SP:2dd943d37d914575aa6ac4f3948a2d5d9f53a8d9 |
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System | Designing task-oriented dialogue systems is a challenging research topic , since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility . Many previous works trained end-to-end ( E2E ) models with supervised learning ( SL ) , however , the bias in annotated system ... | The paper looks the problem of lack of comprehensibility that arises when we use RL to train a E2E dialog system to maximise a given reward function. The paper proposes a HRL/options framework based method to learn a dialog policy over learned latent dialog acts which can then guide the lower level NLG. This along wit... | SP:e8863d56eb4be6ed7aa17241af9ee376570d0770 |
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System | Designing task-oriented dialogue systems is a challenging research topic , since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility . Many previous works trained end-to-end ( E2E ) models with supervised learning ( SL ) , however , the bias in annotated system ... | This paper proposes modeling the hierarchical structure between dialog policy and natural language generator with option network and train it with HRL. It also introduces a discriminator modeled with language models as an additional reward, which further improves the learning procedure's comprehensibility. Besides, thi... | SP:e8863d56eb4be6ed7aa17241af9ee376570d0770 |
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System | Designing task-oriented dialogue systems is a challenging research topic , since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility . Many previous works trained end-to-end ( E2E ) models with supervised learning ( SL ) , however , the bias in annotated system ... | Authors applied reinforcement learning framework to the problem of task-oriented dialog. In particular, they used the option framework to represent the connection between the dialog policy and the natural language generation. Theoretically, they showed that synchronized updates to the low-level and high-level policy ma... | SP:e8863d56eb4be6ed7aa17241af9ee376570d0770 |
On Self-Supervised Image Representations for GAN Evaluation | 1 INTRODUCTION . Generative adversarial networks ( GANs ) are an extremely active research direction in machine learning . The intensive development of the field requires established quantitative measures to assess constantly appearing models . While a large number of evaluation protocols were proposed ( Borji , 2019 ;... | Overview of paper: this work compares supervised feature extractors vs. two types of self-supervised feature extractors for the task of GAN model evaluation. It shows that the ranking provided by self-supervised features is different from that of supervised features, and claims it corresponds better with human judgemen... | SP:c43f864a3d2c7be9c5aa4c2d0f30d3678c80376b |
On Self-Supervised Image Representations for GAN Evaluation | 1 INTRODUCTION . Generative adversarial networks ( GANs ) are an extremely active research direction in machine learning . The intensive development of the field requires established quantitative measures to assess constantly appearing models . While a large number of evaluation protocols were proposed ( Borji , 2019 ;... | This paper proposes to use image representations from trained self-supervised models to evaluate GANs more accurately. Compared to the currently used representations from supervised-pretrained models e.g. InceptionV3, the authors claim, that such embeddings suppress information not critical for the classification proce... | SP:c43f864a3d2c7be9c5aa4c2d0f30d3678c80376b |
On Self-Supervised Image Representations for GAN Evaluation | 1 INTRODUCTION . Generative adversarial networks ( GANs ) are an extremely active research direction in machine learning . The intensive development of the field requires established quantitative measures to assess constantly appearing models . While a large number of evaluation protocols were proposed ( Borji , 2019 ;... | The papers looks at the problem of evaluating GAN samples. Current methods, such as FID/PR with Inception v3 are problematic because they generally depend on using the features of a model discriminatively trained on (a super set of) ImageNet. The authors show that these type of models ignore details that are meaningful... | SP:c43f864a3d2c7be9c5aa4c2d0f30d3678c80376b |
Data Instance Prior for Transfer Learning in GANs | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) are at the forefront of modern high-quality image synthesis in recent years ( Brock et al. , 2018 ; Karras et al. , 2020b ; 2019 ) . GANs have also demonstrated excellent performance on many related computer vision tasks such as image manipulation ( Zhu et al. ,... | The paper focuses on improving the performance of training generative adversarial networks (GANs) with limited target data. With the low diversity and quality when traing GANs with few data, the paper proposes to use data instance prior to reduce the overfitting. Specially, taking the target sample as input, the data... | SP:f5ac44287ac769114d4b4d8dce60c61bfc43ef69 |
Data Instance Prior for Transfer Learning in GANs | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) are at the forefront of modern high-quality image synthesis in recent years ( Brock et al. , 2018 ; Karras et al. , 2020b ; 2019 ) . GANs have also demonstrated excellent performance on many related computer vision tasks such as image manipulation ( Zhu et al. ,... | This paper illustrates how they train GANs with small sample sizes with the help of Transfer Learning. The paper tackled a very specific problem: what should we do with a small sample training size if we want to train a GAN. The authors have supported their arguments by a proof in Data In Prior and experiment results. ... | SP:f5ac44287ac769114d4b4d8dce60c61bfc43ef69 |
Data Instance Prior for Transfer Learning in GANs | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) are at the forefront of modern high-quality image synthesis in recent years ( Brock et al. , 2018 ; Karras et al. , 2020b ; 2019 ) . GANs have also demonstrated excellent performance on many related computer vision tasks such as image manipulation ( Zhu et al. ,... | This submission deals with transfer learning for training GANs with limited label data. The challenge is that training with limited data can result in mode collapse. This submission proposes to use data priors for each instance of the target distribution, transformed through knowledge from a source domain, as condition... | SP:f5ac44287ac769114d4b4d8dce60c61bfc43ef69 |
Network Architecture Search for Domain Adaptation | 1 INTRODUCTION . Supervised machine learning models ( Φ ) aim to minimize the empirical test error ( ( Φ ( x ) , y ) ) by optimizing Φ on training data ( x ) and ground truth labels ( y ) , assuming that the training and testing data are sampled i.i.d from the same distribution . While in practical , the training and t... | This paper introduces an approach to search for the best network architecture for a domain adaptation task. This is achieved by following a differentiable architecture search strategy in which an additional loss function is included to account for the domain shift. Specifically, the loss function aims to minimize the d... | SP:81a8951f6c1d60ee080b72d0e5c5c33425002ee5 |
Network Architecture Search for Domain Adaptation | 1 INTRODUCTION . Supervised machine learning models ( Φ ) aim to minimize the empirical test error ( ( Φ ( x ) , y ) ) by optimizing Φ on training data ( x ) and ground truth labels ( y ) , assuming that the training and testing data are sampled i.i.d from the same distribution . While in practical , the training and t... | In the work, the authors aim at improving the transferability of domain adaptation models from the perspective of neural architecture search. It consists of two phases, in particular, the first phase searches a neural architecture for domain adaptation based on a famous differentiable NAS method named DARTs, and the se... | SP:81a8951f6c1d60ee080b72d0e5c5c33425002ee5 |
Network Architecture Search for Domain Adaptation | 1 INTRODUCTION . Supervised machine learning models ( Φ ) aim to minimize the empirical test error ( ( Φ ( x ) , y ) ) by optimizing Φ on training data ( x ) and ground truth labels ( y ) , assuming that the training and testing data are sampled i.i.d from the same distribution . While in practical , the training and t... | This work devises a two step process for searching optimal models for unsupervised domain adaptation. The first step involves a modification of DARTS where a discrepancy term between features for source and target domain is added to the negative-reward. The obtained feature transformer is then re-trained with an advers... | SP:81a8951f6c1d60ee080b72d0e5c5c33425002ee5 |
Learning Neural Event Functions for Ordinary Differential Equations | 1 INTRODUCTION . Event handling in the context of solving ordinary differential equations ( Shampine & Thompson , 2000 ) allows the user to specify a termination criteria using an event function . Part of the reason is to introduce discontinuous changes to a system that can not be modeled by an ODE alone . Examples bei... | This authors extend neural ODEs to implicitly defined termination criteria modelled by 'neural event functions'. This allows neural ODEs to model abrupt changes in the dynamics (such as collisions or switching dynamics). The authors present how the even handling can be differentiated through, and include a representati... | SP:aa1d40b52346a894713fb9d4114811a5cff237df |
Learning Neural Event Functions for Ordinary Differential Equations | 1 INTRODUCTION . Event handling in the context of solving ordinary differential equations ( Shampine & Thompson , 2000 ) allows the user to specify a termination criteria using an event function . Part of the reason is to introduce discontinuous changes to a system that can not be modeled by an ODE alone . Examples bei... | This work provides an extension to the neural ODEs framework to include discrete changes (i.e. switching) in continuous-time dynamics. The authors provide a few examples of such systems (bouncing balls, collisions of particles, discrete control systems) and derive formally the gradients with respect to the unknown swit... | SP:aa1d40b52346a894713fb9d4114811a5cff237df |
Learning Neural Event Functions for Ordinary Differential Equations | 1 INTRODUCTION . Event handling in the context of solving ordinary differential equations ( Shampine & Thompson , 2000 ) allows the user to specify a termination criteria using an event function . Part of the reason is to introduce discontinuous changes to a system that can not be modeled by an ODE alone . Examples bei... | This paper presents Neural Event ODEs, a method to extend Neural ODEs for modeling discontinuous dynamics in a continuous-time system. Neural Event ODEs allow to learn termination criteria dependent on the system's state while being fully differentiable. Experiments on time series and temporal point processes validate... | SP:aa1d40b52346a894713fb9d4114811a5cff237df |
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations | 1 INTRODUCTION . Generative models have the potential to capture rich representations of data and use them to generate realistic outputs . In particular , Variational AutoEncoders ( VAEs ) ( Kingma & Welling , 2014 ) can capture important properties of high-dimensional data in their latent embeddings , and sample from ... | The paper extends the variational autoencoder framework with a richer prior distribution to model more complex correlations in the latent variable distribution. They start with a Gaussian mixture distribution as the prior for the latent variables, and add an encoder network to allow richer correlation structure in the ... | SP:8cf8825b4a9cf5611a0477a5c18ed60d8f0b052a |
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations | 1 INTRODUCTION . Generative models have the potential to capture rich representations of data and use them to generate realistic outputs . In particular , Variational AutoEncoders ( VAEs ) ( Kingma & Welling , 2014 ) can capture important properties of high-dimensional data in their latent embeddings , and sample from ... | The paper introduces an additional prior-encoder network to autoencoders to learn an unconstrained prior. The autoencoder and prior-encoder networks are iteratively trained with the sliced Wasserstein distance (SWD). To strengthen SWD, this paper further applies nonlinear transformations with a structural consistency t... | SP:8cf8825b4a9cf5611a0477a5c18ed60d8f0b052a |
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations | 1 INTRODUCTION . Generative models have the potential to capture rich representations of data and use them to generate realistic outputs . In particular , Variational AutoEncoders ( VAEs ) ( Kingma & Welling , 2014 ) can capture important properties of high-dimensional data in their latent embeddings , and sample from ... | This paper addresses the issues of representation learning with VAEs and propose EPSWAE as a solution. EPSWAE applies a prior encoder to construct an implicit prior, which is more flexible. Moreover, the authors apply the sliced Wasserstein distance for the matching between the posterior and the prior, enhance the conv... | SP:8cf8825b4a9cf5611a0477a5c18ed60d8f0b052a |
Channel-Directed Gradients for Optimization of Convolutional Neural Networks | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) is currently the dominant algorithm for optimizing large-scale convolutional neural networks ( CNNs ) ( LeCun et al . ( 1998 ) ; Simonyan & Zisserman ( 2014 ) ; He et al . ( 2016b ) ) . Although there has been large activity in optimization methods seeking to improve... | In this paper propose a stochastic optimization method for CNNs, which can obtain an improvement in terms of generation error. The proposed method is used to compute the gradient with respect to output-channel directed re-weighted matrices or Sobolev metrics, and some anabasis is also provided. Some experimental result... | SP:dfb0838d0bd978aeff5b8b6b165d86b7e28ceccc |
Channel-Directed Gradients for Optimization of Convolutional Neural Networks | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) is currently the dominant algorithm for optimizing large-scale convolutional neural networks ( CNNs ) ( LeCun et al . ( 1998 ) ; Simonyan & Zisserman ( 2014 ) ; He et al . ( 2016b ) ) . Although there has been large activity in optimization methods seeking to improve... | The paper proposes a modification of the gradients of objective function wrt model parameters by changing the underling metric on the manifold of model parameters. It proposes two metrics, a reweighed L2 metric, and a specific Sobolev metric, and presents how to compute modified gradients in a compute efficient manner... | SP:dfb0838d0bd978aeff5b8b6b165d86b7e28ceccc |
Channel-Directed Gradients for Optimization of Convolutional Neural Networks | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) is currently the dominant algorithm for optimizing large-scale convolutional neural networks ( CNNs ) ( LeCun et al . ( 1998 ) ; Simonyan & Zisserman ( 2014 ) ; He et al . ( 2016b ) ) . Although there has been large activity in optimization methods seeking to improve... | This paper follows a very active line of research on gradient-based optimization methods for convolutional neural networks. In particular, this work proposes a method that can be applied as an extension to popular optimization methods such as SGD and Adam. The main idea is to introduce a modification of the tensor spac... | SP:dfb0838d0bd978aeff5b8b6b165d86b7e28ceccc |
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | 1 INTRODUCTION . Humans are capable of continuously learning novel tasks by leveraging their lifetime knowledge and expanding them when they encounter a new experience . They can remember the majority of their prior knowledge despite the never-ending nature of their learning process by simply keeping a running tally of... | This paper proposes a method for continual learning of a sequence of supervised tasks which is based on memory-replay for remembering evidence from previously made decisions. This evidence relies on explanations of those decisions rather than data. These explanations are incorporated directly into the loss function. Th... | SP:4d770cdb47df15333de256052add8a4639294809 |
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | 1 INTRODUCTION . Humans are capable of continuously learning novel tasks by leveraging their lifetime knowledge and expanding them when they encounter a new experience . They can remember the majority of their prior knowledge despite the never-ending nature of their learning process by simply keeping a running tally of... | The proposed technique is simple. It helps existing replay methods to boost their accuracy for class-incremental learning. In addition to save some training examples of previous tasks or classes, it also saves the saliency maps of these examples as explanations of classification. The size the saliency maps is small and... | SP:4d770cdb47df15333de256052add8a4639294809 |
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | 1 INTRODUCTION . Humans are capable of continuously learning novel tasks by leveraging their lifetime knowledge and expanding them when they encounter a new experience . They can remember the majority of their prior knowledge despite the never-ending nature of their learning process by simply keeping a running tally of... | This paper tackles the problem of catastrophic forgetting in a continual learning scenario, in which the same classifier is trained incrementally on new classification tasks, each defined on a new set of output classes, and asked to retain performance on all the previous tasks. To tackle this problem, a stream of appro... | SP:4d770cdb47df15333de256052add8a4639294809 |
Grounded Compositional Generalization with Environment Interactions | 1 INTRODUCTION . Compositional generalization is a key skill for flexible and efficient learning . Humans leverage compositionality to create and recognize new combinations of familiar concepts ( Chomsky , 1957 ; Minsky , 1986 ) . Though there are many progresses for machine learning and deep learning in various areas ... | This paper tries to address a very important problem, compositional generalization in grounded agent instruction learning. It proposes to use interactions between agent and the environment to define output components, and entropy regularization to reduce redundant dependency on input. It shows significant improvements ... | SP:0117ddd034d838c0c7e226111c52c13712cb454d |
Grounded Compositional Generalization with Environment Interactions | 1 INTRODUCTION . Compositional generalization is a key skill for flexible and efficient learning . Humans leverage compositionality to create and recognize new combinations of familiar concepts ( Chomsky , 1957 ; Minsky , 1986 ) . Though there are many progresses for machine learning and deep learning in various areas ... | The paper proposes a new regularization method that constrains the mapping between the inputs and output spaces for achieving compositional generalization in simple grounded environments like gSCAN. The problem is interesting and important and the paper is corroborated by good experiments with 25% accuracy increase and... | SP:0117ddd034d838c0c7e226111c52c13712cb454d |
Grounded Compositional Generalization with Environment Interactions | 1 INTRODUCTION . Compositional generalization is a key skill for flexible and efficient learning . Humans leverage compositionality to create and recognize new combinations of familiar concepts ( Chomsky , 1957 ; Minsky , 1986 ) . Though there are many progresses for machine learning and deep learning in various areas ... | This paper proposes a new model for the gSCAN dataset (Ruis et al. 2020) which is a synthetically-generated dataset that challenges models to generalize to new compositions of attributes and objects in an instruction. The paper proposes to use "entropy regularization" as a way to enforce that spurious correlations betw... | SP:0117ddd034d838c0c7e226111c52c13712cb454d |
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation | 1 INTRODUCTION . Depth estimation is a long standing problem in computer vision community , which offers useful information to a wide range of tasks including robotic perception , augmented reality and autonomous driving , etc . Compared with depth estimation methods which rely on active vision or multi-view paradigms ... | This paper proposed a novel framework to improve self-supervised monocular depth estimation leveraging semantic features at local and global level. The proposed framework includes a semantic-guided edge enhancement module to extract and enhance point-based features around semantic boundaries. The proposed framework als... | SP:529dd8f14cc4459b84dcb6a2eb5a35a520e10124 |
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation | 1 INTRODUCTION . Depth estimation is a long standing problem in computer vision community , which offers useful information to a wide range of tasks including robotic perception , augmented reality and autonomous driving , etc . Compared with depth estimation methods which rely on active vision or multi-view paradigms ... | The authors tackle the problem of self-supervised depth estimation and particularly address the issue of poor depth estimation on object boundaries. The introduction motivates the problem well, and the related work covers most of the relevant papers. The authors propose two main modifications allowing them to leverage ... | SP:529dd8f14cc4459b84dcb6a2eb5a35a520e10124 |
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation | 1 INTRODUCTION . Depth estimation is a long standing problem in computer vision community , which offers useful information to a wide range of tasks including robotic perception , augmented reality and autonomous driving , etc . Compared with depth estimation methods which rely on active vision or multi-view paradigms ... | The paper presents a method for semantic-guided self-supervised depth estimation from monocular images. They propose semantic guidance to improve depth estimation performance. This is obtained by applying semantic guidance at multiple levels in the decoder via an attention layer and via feature enhancement in edges are... | SP:529dd8f14cc4459b84dcb6a2eb5a35a520e10124 |
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors | 1 INTRODUCTION . Imaging inverse problems seek to recover an unknown image x 2 Rn from its noisy measurements y 2 Rm . Such problems arise in many fields , ranging from low-level computer vision to biomedical imaging . Since many imaging inverse problems are ill-posed , it is common to regularize the solution by using ... | This paper proposed for the first time the asynchronous variants of deterministic and stochastic regularization-by-denoising (RED) algorithms which have become popular recently in image recovery and reconstruction applications since they leverage the power of pretrained deep denoising neural networks into the traditio... | SP:db54672a85a9533d2afb420b9deb50e905bd33ec |
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors | 1 INTRODUCTION . Imaging inverse problems seek to recover an unknown image x 2 Rn from its noisy measurements y 2 Rm . Such problems arise in many fields , ranging from low-level computer vision to biomedical imaging . Since many imaging inverse problems are ill-posed , it is common to regularize the solution by using ... | Due to the growth of data sets in a lot of applications, it is important to develop algorithms to achieve great performance but with significantly reduced computational cost. The paper proposes asynchronous type of parallel algorithms by combining the pre-trained deep denoisers. In particular, batch gradient and stocha... | SP:db54672a85a9533d2afb420b9deb50e905bd33ec |
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors | 1 INTRODUCTION . Imaging inverse problems seek to recover an unknown image x 2 Rn from its noisy measurements y 2 Rm . Such problems arise in many fields , ranging from low-level computer vision to biomedical imaging . Since many imaging inverse problems are ill-posed , it is common to regularize the solution by using ... | The paper describes a novel implementation of RED, regularization by denoising, which better leverages multicore architectures to achieve a significant speedup. The proposed implementation splits the gradient step into smaller components, which can each be executed independently on different cores and then used to upda... | SP:db54672a85a9533d2afb420b9deb50e905bd33ec |
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | 1 INTRODUCTION . It is widely known that deep neural networks ( DNNs ) are vulnerable to adversarial examples , which are crafted via perturbing clean examples to cause the victim model to make incorrect predictions . In a white-box setting where the adversaries have full access to the architecture and parameters of th... | This paper proposes a new hard-label black-box adversarial attack method based on reinforcement learning. The authors formulate the black-box attacking problem as a reinforcement learning problem, and design a policy network to learn the appropriate attack directions, in order to achieve more efficient attacks. The pro... | SP:6b7fdf95219b32e4dca1b3fb084f2c11a5d29fd9 |
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | 1 INTRODUCTION . It is widely known that deep neural networks ( DNNs ) are vulnerable to adversarial examples , which are crafted via perturbing clean examples to cause the victim model to make incorrect predictions . In a white-box setting where the adversaries have full access to the architecture and parameters of th... | This work proposes to formulate the problem of black-box adversarial attacks as learning a policy network that predicts offsets to some initial guesses of adversarial examples. The proposed PDA method tackles the problematic situation of queries with only hard labels. A specific reward, architecture, and pretraining se... | SP:6b7fdf95219b32e4dca1b3fb084f2c11a5d29fd9 |
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | 1 INTRODUCTION . It is widely known that deep neural networks ( DNNs ) are vulnerable to adversarial examples , which are crafted via perturbing clean examples to cause the victim model to make incorrect predictions . In a white-box setting where the adversaries have full access to the architecture and parameters of th... | This paper proposes a new hard-label black-box adversarial attack method based on reinforcement learning. The general idea is to improve the adversarial noise compressing efficiency taking advantage of past queries and the policy network. Experiments are conducted on MNIST, CIFAR-10 and ImageNet and achieved superior p... | SP:6b7fdf95219b32e4dca1b3fb084f2c11a5d29fd9 |
Adaptive Federated Optimization | 1 INTRODUCTION . Federated learning ( FL ) is a machine learning paradigm in which multiple clients cooperate to learn a model under the orchestration of a central server ( McMahan et al. , 2017 ) . In FL , raw client data is never shared with the server or other clients . This distinguishes FL from traditional distrib... | This paper studies the convergence of well-known adaptive methods, ADAM, ADAGRAD, and YOGI, for the federated learning problem. In particular, while the nodes (clients) still use SGD for their local computations (same as Fed-Avg), the server uses one of the three adaptive methods mentioned above to update the model. Th... | SP:c95a81c7f0df69cd0a7a85b08e8ed5c610732f8a |
Adaptive Federated Optimization | 1 INTRODUCTION . Federated learning ( FL ) is a machine learning paradigm in which multiple clients cooperate to learn a model under the orchestration of a central server ( McMahan et al. , 2017 ) . In FL , raw client data is never shared with the server or other clients . This distinguishes FL from traditional distrib... | This paper presents an adaptive federated optimization framework that induces three different adaptive federated learning algorithms, which are proposed to address the issues of client drift due to data heterogeneity and lack of adaptivity. The authors presented thorough literature survey on the federated learning and ... | SP:c95a81c7f0df69cd0a7a85b08e8ed5c610732f8a |
Adaptive Federated Optimization | 1 INTRODUCTION . Federated learning ( FL ) is a machine learning paradigm in which multiple clients cooperate to learn a model under the orchestration of a central server ( McMahan et al. , 2017 ) . In FL , raw client data is never shared with the server or other clients . This distinguishes FL from traditional distrib... | This paper extends the server model averaging step in FedAvg to a more general adaptive optimization step on the global model, specifically, by writing the model averaging as a gradient descent step using a pseudo gradient. Three variants of this scheme (FedOpt) are presented, based on three adaptive optimizers, includ... | SP:c95a81c7f0df69cd0a7a85b08e8ed5c610732f8a |
Counterfactual Generative Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are the main building blocks of many state-of-the-art machine learning systems that address diverse tasks such as image classification ( He et al. , 2016 ) , natural language processing ( Brown et al. , 2020 ) , and autonomous driving ( Ohn-Bar et al. , 2020 ) . Despite th... | The main idea of the paper, i.e., using independent causal mechanisms to generate interventional images, has already been explored by Kocaoglu et al. in Causalgan: Learning causal implicit generative models with adversarial training, ICLR'18. Same as here, the authors there also "view image generation as a causal proce... | SP:a1c087b38201c94a7fccb11826606bbf678a8a57 |
Counterfactual Generative Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are the main building blocks of many state-of-the-art machine learning systems that address diverse tasks such as image classification ( He et al. , 2016 ) , natural language processing ( Brown et al. , 2020 ) , and autonomous driving ( Ohn-Bar et al. , 2020 ) . Despite th... | Deep neural network brittleness can be attributed to their tendency to latch on to spurious correlations in the training dataset. The proposal in the paper is to learn to generate samples where these correlations can be eliminated. To this end, the authors, distill trained conditional big gan into a transformation with... | SP:a1c087b38201c94a7fccb11826606bbf678a8a57 |
Counterfactual Generative Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are the main building blocks of many state-of-the-art machine learning systems that address diverse tasks such as image classification ( He et al. , 2016 ) , natural language processing ( Brown et al. , 2020 ) , and autonomous driving ( Ohn-Bar et al. , 2020 ) . Despite th... | This paper proposes a new generative model that generate images from 3 seperate aspects: foreground masks (shapes), forground texture, and backgrounds. Then they convexly mix these 3 aspects into one image. By doing so, they can vary each aspect individually without changing other aspects, enabling the model to generat... | SP:a1c087b38201c94a7fccb11826606bbf678a8a57 |
Federated Learning With Quantized Global Model Updates | 1 Introduction . Federated learning ( FL ) enables wireless devices to collaboratively train a global model by utilizing locally available data and computational capabilities under the coordination of a parameter server ( PS ) while the data never leaves the devices McMahan & Ramage ( 2017 ) . In FL with M devices the ... | This paper studies federated learning with quantization. The problem setting is very standard, including both iid and non-iid cases. This work proposes a new algorithm, called lossy FL, to save the communication costs, especially from the broadcasting direction. To my understanding, the algorithm is new but still very ... | SP:4b3c0127fcd5cc73226d2b8f58df658f5599791e |
Federated Learning With Quantized Global Model Updates | 1 Introduction . Federated learning ( FL ) enables wireless devices to collaboratively train a global model by utilizing locally available data and computational capabilities under the coordination of a parameter server ( PS ) while the data never leaves the devices McMahan & Ramage ( 2017 ) . In FL with M devices the ... | This paper suggests a lossy federated learning (LFL) algorithm where the PS broadcasts a quantized version of the global model to devices. This approach helps reduce the communication cost of federated learning and is in particular useful when the communication bandwidth is limited. Associated convergence analysis is g... | SP:4b3c0127fcd5cc73226d2b8f58df658f5599791e |
Federated Learning With Quantized Global Model Updates | 1 Introduction . Federated learning ( FL ) enables wireless devices to collaboratively train a global model by utilizing locally available data and computational capabilities under the coordination of a parameter server ( PS ) while the data never leaves the devices McMahan & Ramage ( 2017 ) . In FL with M devices the ... | In the setting of Federated Learning, the authors propose to quantize both (1) the model send from PS to devices, and (2) the update from device to PS. Although the idea of model-broadcast-compression has appeared in previous work, the authors improve upon previous works in that (1) the authors propose to compress-and-... | SP:4b3c0127fcd5cc73226d2b8f58df658f5599791e |
Measuring and mitigating interference in reinforcement learning | 1 INTRODUCTION . Generalization is a key property of reinforcement learning ( RL ) algorithms with function approximation . An agent must correctly generalize its recent experience to both states it has not yet encountered and other states it encountered in the past . Generalization has been extensively studied in supe... | This paper studies the reason for interference, aka catastrophic forgetting, when using parametric models for Reinforcement Learning. The authors draw the connection with previous methods and introduce some reasonable measure of interference. Then, they introduce a method to explicitly address the problem of interferen... | SP:1df24e8b97ffad63accec0ed8a3477c211601fd5 |
Measuring and mitigating interference in reinforcement learning | 1 INTRODUCTION . Generalization is a key property of reinforcement learning ( RL ) algorithms with function approximation . An agent must correctly generalize its recent experience to both states it has not yet encountered and other states it encountered in the past . Generalization has been extensively studied in supe... | The paper studies interference and forgetting in the context of reinforcement learning (RL). On the example of the Iterative Value Estimation family of algorithms, the authors define interference as the increase of the true Q target prediction error after updating Q function parameters. Since the true Q target is usua... | SP:1df24e8b97ffad63accec0ed8a3477c211601fd5 |
Measuring and mitigating interference in reinforcement learning | 1 INTRODUCTION . Generalization is a key property of reinforcement learning ( RL ) algorithms with function approximation . An agent must correctly generalize its recent experience to both states it has not yet encountered and other states it encountered in the past . Generalization has been extensively studied in supe... | This paper studies the the interference problem under the API and FPI setting. It designs new measure for interference, and shows that the interference measure is correlated with forgetting. With the help of the interference measure, the paper studies the importance of the final layer of the neural network and proposes... | SP:1df24e8b97ffad63accec0ed8a3477c211601fd5 |
Learning and Generalization in Univariate Overparameterized Normalizing Flows | 1 INTRODUCTION . Neural network models trained using simple first-order iterative algorithms have been very effective in both supervised and unsupervised learning . Theoretical reasoning of this phenomenon requires one to consider simple but quintessential formulations , where this can be demonstrated by mathematical p... | This paper proved that for a certain modified version of sufficiently-overparametrized univariate normalizing flows where the underlying neural network has only one hidden layer, with high probability it can learn a distribution that is close enough to the target distribution where the distance can be measured in, e.g.... | SP:8180451eca79f017df91f0066ee46d8b149eaa48 |
Learning and Generalization in Univariate Overparameterized Normalizing Flows | 1 INTRODUCTION . Neural network models trained using simple first-order iterative algorithms have been very effective in both supervised and unsupervised learning . Theoretical reasoning of this phenomenon requires one to consider simple but quintessential formulations , where this can be demonstrated by mathematical p... | This paper studies overparameterization over unsupervised learning. In detail, it uses constrained normalizing flows (CNF) and unconstrained normalizing flows (UNF) to learn the underlying unknown one-dimensional distribution, which can be parameterized by a two-layer neural network. The authors propose theoretical res... | SP:8180451eca79f017df91f0066ee46d8b149eaa48 |
Learning and Generalization in Univariate Overparameterized Normalizing Flows | 1 INTRODUCTION . Neural network models trained using simple first-order iterative algorithms have been very effective in both supervised and unsupervised learning . Theoretical reasoning of this phenomenon requires one to consider simple but quintessential formulations , where this can be demonstrated by mathematical p... | The paper studies the role of overparameterization in learning normalizing flow models. More specifically, the authors analyze the optimization and generalization of such a model when the transport map f is parameterized by a two-layer neural network with potentially many hidden units (or highly over-parameterized). Im... | SP:8180451eca79f017df91f0066ee46d8b149eaa48 |
Learning Generalizable Visual Representations via Interactive Gameplay | 1 INTRODUCTION . We are interested in studying what facets of their environment artificial agents learn to represent through interaction and gameplay . We study this question within the context of hide-and-seek , for which proficiency requires an ability to navigate around in an environment and manipulate objects as we... | This paper examines the representations learned during adversarial gameplay, specifically a hide-and-seek game called Cache. The hiding agent must place an object in a room such that the seeker agent cannot find it. The authors argue that the adversarial nature of the game shapes the representations. Inspired by psy... | SP:6230795b336182834de7e33ddc67aa69cba3efa4 |
Learning Generalizable Visual Representations via Interactive Gameplay | 1 INTRODUCTION . We are interested in studying what facets of their environment artificial agents learn to represent through interaction and gameplay . We study this question within the context of hide-and-seek , for which proficiency requires an ability to navigate around in an environment and manipulate objects as we... | This paper proposes embodied game-playing with artificial agents as a method to learn better representations of their environment. They describe a game, cache, which is a variant of hide-and-seek played in a virtual environment and a method for training an agent to play the game. They present results which demonstrate ... | SP:6230795b336182834de7e33ddc67aa69cba3efa4 |
Learning Generalizable Visual Representations via Interactive Gameplay | 1 INTRODUCTION . We are interested in studying what facets of their environment artificial agents learn to represent through interaction and gameplay . We study this question within the context of hide-and-seek , for which proficiency requires an ability to navigate around in an environment and manipulate objects as we... | In this paper, the author's propose an embodied adversarial reinforcement learning agent that can play a variation of hide-and-seek called Cache. This environment is a high fidelity interactive world. The authors argue that the agents are able to learn flexible representations of their observations which encode informa... | SP:6230795b336182834de7e33ddc67aa69cba3efa4 |
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces | 1 INTRODUCTION . Despite achieving exceptional performance in benign settings , modern machine learning models have been shown to be highly vulnerable to inputs , known as adversarial examples , crafted with targeted but imperceptible perturbations ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . This discovery h... | The authors generalized the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures with $\ell_p$ metric structures $p\geq 2$. Building on the generalized inequality, they propose a sample-based algorithm to estimate the concentration of measure using half-spaces. The main contribution is Theorem 3.3 follo... | SP:4ec13fd58b0cea4ef01b329c45b6c0042bc9f951 |
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces | 1 INTRODUCTION . Despite achieving exceptional performance in benign settings , modern machine learning models have been shown to be highly vulnerable to inputs , known as adversarial examples , crafted with targeted but imperceptible perturbations ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . This discovery h... | The authors consider the problem of estimating intrinsic robustness using data samples. At a high level, intrinsic robustness is a measure that indicates the probability that a noisy version of a covariate would not be mislabeled. Mahloujifar et al. (2019a) have shown that estimating intrinsic robustness is closely rel... | SP:4ec13fd58b0cea4ef01b329c45b6c0042bc9f951 |
Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces | 1 INTRODUCTION . Despite achieving exceptional performance in benign settings , modern machine learning models have been shown to be highly vulnerable to inputs , known as adversarial examples , crafted with targeted but imperceptible perturbations ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . This discovery h... | This paper considers the estimation of the concentration of measures, which possibly causes of the vulnerability of machine learning models to adversarial attacks. Towards such a goal, the authors first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary l_p norm. An algorithm ... | SP:4ec13fd58b0cea4ef01b329c45b6c0042bc9f951 |
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning | 1 INTRODUCTION Benchmarks have played a crucial role in advancing entire research fields , for instance computer vision with the introduction of CIFAR-10 and ImageNet ( Krizhevsky et al. , 2009 ; 2012 ) . When it comes to the field of reinforcement learning ( RL ) , similar breakthroughs have been achieved in domains s... | This paper proposed a new benchmark for studying reinforcement learning and its generalization in the context of the robotic manipulation problem. To study the generalization of a learned policy, the proposed benchmark is equipped with an interface that makes intervention easy. This interface helps to define a training... | SP:e37da841052cbdd81b629bdb5c126aa1a375d7e7 |
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning | 1 INTRODUCTION Benchmarks have played a crucial role in advancing entire research fields , for instance computer vision with the introduction of CIFAR-10 and ImageNet ( Krizhevsky et al. , 2009 ; 2012 ) . When it comes to the field of reinforcement learning ( RL ) , similar breakthroughs have been achieved in domains s... | Motivated by the difficulty of evaluating RL’s ability to transfer behaviors across environments, the authors propose the CausalWorld benchmark. Unlike prior benchmarks, CausalWorld exposes well-defined casual variables, in the form of task factors, and focuses on robotic manipulation of an open-source robot platform. ... | SP:e37da841052cbdd81b629bdb5c126aa1a375d7e7 |
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning | 1 INTRODUCTION Benchmarks have played a crucial role in advancing entire research fields , for instance computer vision with the introduction of CIFAR-10 and ImageNet ( Krizhevsky et al. , 2009 ; 2012 ) . When it comes to the field of reinforcement learning ( RL ) , similar breakthroughs have been achieved in domains s... | This paper proposes a a robotic manipulation benchmark for causal structure and transfer learning in a simulation environment considering 3D shape construction tasks given a set of blocks. Baseline results using model-free algorithms are provided for chosen tasks, e.g. pushing, picking, pick&place, stacking. It is also... | SP:e37da841052cbdd81b629bdb5c126aa1a375d7e7 |
Monte-Carlo Planning and Learning with Language Action Value Estimates | 1 INTRODUCTION . Building an intelligent goal-oriented agent that can perceive and react via natural language is one of the grand challenges of artificial intelligence . In pursuit of this goal , we consider Interactive Fiction ( IF ) games ( Nelson , 2001 ; Montfort , 2005 ) , which are text-based simulation environme... | This paper presents a novel MCTS-based policy improvement operator called MC-LAVE designed specifically for environments with text-based action spaces. MC-LAVE adds an additional term to PUCT that shares information across semantically similar actions. This additional term for a given action $a$ is set to the soft maxi... | SP:c8ac9e83702c206ede5bdf55988a5fb5fe73f39b |
Monte-Carlo Planning and Learning with Language Action Value Estimates | 1 INTRODUCTION . Building an intelligent goal-oriented agent that can perceive and react via natural language is one of the grand challenges of artificial intelligence . In pursuit of this goal , we consider Interactive Fiction ( IF ) games ( Nelson , 2001 ; Montfort , 2005 ) , which are text-based simulation environme... | This paper presents a method for combining planning an learning in text-based games. In particular it augments Monte-Carlo Tree Search to include a language-similarity bonus to encourage exploration of similar actions. This bonus works by computing a Language Action Value Estimate - which is based on increasing the sco... | SP:c8ac9e83702c206ede5bdf55988a5fb5fe73f39b |
Monte-Carlo Planning and Learning with Language Action Value Estimates | 1 INTRODUCTION . Building an intelligent goal-oriented agent that can perceive and react via natural language is one of the grand challenges of artificial intelligence . In pursuit of this goal , we consider Interactive Fiction ( IF ) games ( Nelson , 2001 ; Montfort , 2005 ) , which are text-based simulation environme... | This paper introduces Monte-Carlo planning with language action value estimates to guide exploration. The method builds on top of MCTS w/ PUCT, where a policy distribution over actions is introduced to estimate Q for actions not seen during sampling. The modification proposed here is an additional term to the Q estimat... | SP:c8ac9e83702c206ede5bdf55988a5fb5fe73f39b |
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding | 1 INTRODUCTION . Natural scene understanding can be achieved by decomposing the signal into its underlying factors of variation . An intuitive approach for this problem assumes that a visual representation of the world can be constructed via a generative process that receives factors as input and produces natural signa... | The paper addresses the problem of disentangling the underlying generative factors from data with a particular focus on dynamic natural data. It provides evidence that transitions of objects in natural movies can be characterised by temporally sparse distributions. A novel proof based on a sparse prior on temporally ad... | SP:2b10bfa0723c412667263a1db3e1d950a8e361c5 |
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding | 1 INTRODUCTION . Natural scene understanding can be achieved by decomposing the signal into its underlying factors of variation . An intuitive approach for this problem assumes that a visual representation of the world can be constructed via a generative process that receives factors as input and produces natural signa... | The paper starts with an observation that temporal transitions in sequences of natural images are sparse, which is supported by data collected from two big datasets (youtube-vots and kitti-mots). This suggests using a sparse prior for temporal transitions of latent variables when modelling naturalistic scenes. The auth... | SP:2b10bfa0723c412667263a1db3e1d950a8e361c5 |
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding | 1 INTRODUCTION . Natural scene understanding can be achieved by decomposing the signal into its underlying factors of variation . An intuitive approach for this problem assumes that a visual representation of the world can be constructed via a generative process that receives factors as input and produces natural signa... | This paper introduces a novel VAE-based model with the aim to improve unsupervised disentangling of latent factors in visual data. This model differs from previous disentangling models in that it takes short (2-frame) videos as input instead of static images. The model is equipped with a Laplace prior over the dynami... | SP:2b10bfa0723c412667263a1db3e1d950a8e361c5 |
Automated Concatenation of Embeddings for Structured Prediction | 1 INTRODUCTION . Recent developments on pretrained contextualized embeddings have significantly improved the performance of structured prediction tasks in natural language processing . Approaches based on contextualized embeddings , such as ELMo ( Peters et al. , 2018 ) , Flair ( Akbik et al. , 2018 ) , BERT ( Devlin e... | This paper explores a way of learning how to automatically construct a concatenated set of embeddings for structured prediction tasks in NLP. The paper's model takes up to L embeddings concatenated together and feeds them into standard models (BiLSTM-CRFs or the BiLSTM-Biaffine technique of Dozat and Manning) to tackle... | SP:481614a6302380b81ac1248b92c4c12ae366a827 |
Automated Concatenation of Embeddings for Structured Prediction | 1 INTRODUCTION . Recent developments on pretrained contextualized embeddings have significantly improved the performance of structured prediction tasks in natural language processing . Approaches based on contextualized embeddings , such as ELMo ( Peters et al. , 2018 ) , Flair ( Akbik et al. , 2018 ) , BERT ( Devlin e... | This paper introduced an interesting application of reinforcement learning in the selection of concatenation of contextual/non-contextual word embeddings. It is clever to limit the search space on the selection of embedding sources rather than search the whole network structure, as the current strategy is much easier ... | SP:481614a6302380b81ac1248b92c4c12ae366a827 |
Automated Concatenation of Embeddings for Structured Prediction | 1 INTRODUCTION . Recent developments on pretrained contextualized embeddings have significantly improved the performance of structured prediction tasks in natural language processing . Approaches based on contextualized embeddings , such as ELMo ( Peters et al. , 2018 ) , Flair ( Akbik et al. , 2018 ) , BERT ( Devlin e... | This paper proposes to automate the concatenation of word embeddings (obtained using different strategies) to produce powerful word representations for a given downstream task. To this end, the paper develops an approach based on Neural Architecture Search, wherein the search space is comprised of embedding candidates ... | SP:481614a6302380b81ac1248b92c4c12ae366a827 |
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images | 1 INTRODUCTION . Image denoising is one of the oldest problems in image processing and low-level computer vision , yet it still attracts lots of attention due to the fundamental nature of the problem . A vast number of algorithms have been proposed over the past several decades , and recently , the CNN-based methods , ... | This paper proposes a framework to train a network to remove noises, which are zero-mean, additive and independent of the clean image, with only noisy images and without knowing the noise statistics. They mathematically prove that a network, which is trained from pairs of images generated by adding simulated noises int... | SP:dc8873528c8b4cccafa3d45fe0b244a2d0d99e9b |
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images | 1 INTRODUCTION . Image denoising is one of the oldest problems in image processing and low-level computer vision , yet it still attracts lots of attention due to the fundamental nature of the problem . A vast number of algorithms have been proposed over the past several decades , and recently , the CNN-based methods , ... | This paper addresses a challenging task of blind image denoising where a single noisy image is provided with assumption that it is zero mean, additive and independent from the original image content. This is mostly the real-world scenario. Different from the recent N2N training, the authors propose a GAN2GAN based meth... | SP:dc8873528c8b4cccafa3d45fe0b244a2d0d99e9b |
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images | 1 INTRODUCTION . Image denoising is one of the oldest problems in image processing and low-level computer vision , yet it still attracts lots of attention due to the fundamental nature of the problem . A vast number of algorithms have been proposed over the past several decades , and recently , the CNN-based methods , ... | This paper proposed a new method for blind image denoising using a "GAN2GAN" network. Different from previous work noise2noise, the proposed method only needs single noisy images to train the network, without noisy pairs. Given a noisy dataset, a GAN generator is trained using the real noisy but smooth patches. With th... | SP:dc8873528c8b4cccafa3d45fe0b244a2d0d99e9b |
Learning not to learn: Nature versus nurture in silico | 1 INTRODUCTION . The ’ nature versus nurture ’ debate ( e.g. , Mutti et al. , 1996 ; Tabery , 2014 ) – the question of which aspects of behavior are ’ hard-coded ’ by evolution , and which are learned from experience – is one of the oldest and most controversial debates in biology . Evolutionary principles prescribe th... | This paper provides an analysis of RNN-based meta learning approaches. In particular, it investigates the strategies learned via meta-learning, contrasting strategies involving task-dependent learning vs heuristic or hard-coded solutions. Empirical evidence in two sets of experiments, on a 2-armed bandit toy task and a... | SP:b70f1ee4f3fb0fd89e76cf8f09b038cfe13e7e89 |
Learning not to learn: Nature versus nurture in silico | 1 INTRODUCTION . The ’ nature versus nurture ’ debate ( e.g. , Mutti et al. , 1996 ; Tabery , 2014 ) – the question of which aspects of behavior are ’ hard-coded ’ by evolution , and which are learned from experience – is one of the oldest and most controversial debates in biology . Evolutionary principles prescribe th... | This paper observes that in meta-RL (and evolutionary biology), sometimes it is advantageous to learn behaviors that adapt to the particular task, while other times not adapting to the task, and instead relying on a task-agnostic “hard-coded” behavior is sufficient. While much meta-RL research typically focuses on the ... | SP:b70f1ee4f3fb0fd89e76cf8f09b038cfe13e7e89 |
Learning not to learn: Nature versus nurture in silico | 1 INTRODUCTION . The ’ nature versus nurture ’ debate ( e.g. , Mutti et al. , 1996 ; Tabery , 2014 ) – the question of which aspects of behavior are ’ hard-coded ’ by evolution , and which are learned from experience – is one of the oldest and most controversial debates in biology . Evolutionary principles prescribe th... | .** The authors investigate the question of when the optimal behavior for an agent is to learn from experience versus when the optimal behavior is to apply the same (memorized) policy in every scenario. They begin by introducing a simple bandits environment wherein they derive the optimal policy and identify regimes in... | SP:b70f1ee4f3fb0fd89e76cf8f09b038cfe13e7e89 |
For self-supervised learning, Rationality implies generalization, provably | 1 INTRODUCTION . The current standard approach for classification is “ end-to-end supervised learning ” where one fits a complex ( e.g. , a deep neural network ) classifier to the given training set ( Tan & Le , 2019 ; He et al. , 2016 ) . However , modern classifiers are heavily over parameterized , and as demonstrate... | The present paper aims to understand the generalization capability of self-supervised learning algorithms that fine-tune a simple linear classifier to the labels. Analyzing generalization in this case is challenging due to a data re-use problem: the same training data that is used for self-supervised learning is also u... | SP:280743806ee639df87f4d7de86f287fb455e3ff3 |
For self-supervised learning, Rationality implies generalization, provably | 1 INTRODUCTION . The current standard approach for classification is “ end-to-end supervised learning ” where one fits a complex ( e.g. , a deep neural network ) classifier to the given training set ( Tan & Le , 2019 ; He et al. , 2016 ) . However , modern classifiers are heavily over parameterized , and as demonstrate... | The paper analyzes the generalization gap for self-supervised learning. This paper's contribution includes the proposal of decomposing the generalization bound into three terms: robustness, rationality, and memorization (RRM). The three terms explain the generalization gap with some different perspectives. With the RRM... | SP:280743806ee639df87f4d7de86f287fb455e3ff3 |
For self-supervised learning, Rationality implies generalization, provably | 1 INTRODUCTION . The current standard approach for classification is “ end-to-end supervised learning ” where one fits a complex ( e.g. , a deep neural network ) classifier to the given training set ( Tan & Le , 2019 ; He et al. , 2016 ) . However , modern classifiers are heavily over parameterized , and as demonstrate... | This paper gives a new perspective on generalization, motivated by the success of self-supervised learning, especially on noisy data. They view the generalization error as consisting of 3 independent components: robustness, rationality and memorization. Informally, robustness measures the degradation in training accura... | SP:280743806ee639df87f4d7de86f287fb455e3ff3 |
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents | Discrete latent variables are considered important to model the generation process of real world data , which has motivated research on Variational Autoencoders ( VAEs ) with discrete latents . However , standard VAE training is not possible in this case , which has motivated different strategies to manipulate discrete... | This paper proposes an evolutionary optimization framework for training vartional autoencoders (VAEs) with discrete latents. In contrast to the standard VAE paradigm, the proposed TVAE approach does not require an encoder for amortized inference given the input. The method instead relies on a pool of latent variable sa... | SP:f741d980c9c560a21298e947f1605dcbab7ceeac |
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents | Discrete latent variables are considered important to model the generation process of real world data , which has motivated research on Variational Autoencoders ( VAEs ) with discrete latents . However , standard VAE training is not possible in this case , which has motivated different strategies to manipulate discrete... | the paper proposes a novel approach to training variational autoencoder models, based on non-parametric form of truncated approximate posterior. Posterior is truncated to have support on a small subset of latent space allowing for exact marginalization. The support of approximate posterior in latent space for each data... | SP:f741d980c9c560a21298e947f1605dcbab7ceeac |
Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents | Discrete latent variables are considered important to model the generation process of real world data , which has motivated research on Variational Autoencoders ( VAEs ) with discrete latents . However , standard VAE training is not possible in this case , which has motivated different strategies to manipulate discrete... | This paper proposes a new approach to train VAEs with binary latents, using an evolutionary algorithm to optimise a discrete set of variational parameters rather than the usual amortised variational model trained with gradient-based methods. The authors consider the setting of training a VAE with discrete, Bernoulli di... | SP:f741d980c9c560a21298e947f1605dcbab7ceeac |
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