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Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems | 1 INTRODUCTION . In recent years , many studies have been devoted to explaining the great success of over-parameterized neural networks , where the number of parameters is much larger than that needed to fit a given training dataset . This study also treats over-parameterized two-layer neural networks using smooth acti... | The authors study the problem of binary logistic regression in a two-layer network with a smooth activation function. They introduce a separability assumption on the dataset using the neural tangent model. This separability assumption is weaker than the more Neural Tangent Kernel assumption that has been extensively ... | SP:e4f6ad5fdfa9a438b8b3be4cbe856ea2ab5d68e6 |
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems | 1 INTRODUCTION . In recent years , many studies have been devoted to explaining the great success of over-parameterized neural networks , where the number of parameters is much larger than that needed to fit a given training dataset . This study also treats over-parameterized two-layer neural networks using smooth acti... | This paper studies the training of over-parameterized two layer neural networks with smooth activation functions. In particular, this paper establishes convergence guarantee as well as generalization error bounds under an assumption that the data can be separated by a neural tangent model. The authors also show that th... | SP:e4f6ad5fdfa9a438b8b3be4cbe856ea2ab5d68e6 |
A SPIKING SEQUENTIAL MODEL: RECURRENT LEAKY INTEGRATE-AND-FIRE | 1 INTRODUCTION . The terms of deep learning and the corresponding artificial neural networks ( ANNs ) derivatives have been dominating in subject of computer science and keep the current state-of-the-art performance in a widespread of machine learning ’ s application scenario such as computer vision ( Simonyan & Zisser... | This paper proposes a brain-inspired recurrent neural network architecture, named Recurrent Leaky Integrate-and-Fire (RLIF). Computationally, the model is designed to mimic how biological neurons behave, e.g. producing binary values. The hope is that this will allow such computational models to be easily implemented on... | SP:ef7cf9c0569adc304bdc8601229ac7579178a871 |
A SPIKING SEQUENTIAL MODEL: RECURRENT LEAKY INTEGRATE-AND-FIRE | 1 INTRODUCTION . The terms of deep learning and the corresponding artificial neural networks ( ANNs ) derivatives have been dominating in subject of computer science and keep the current state-of-the-art performance in a widespread of machine learning ’ s application scenario such as computer vision ( Simonyan & Zisser... | Recently, it has been shown that spiking neural networks (SNN) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron (LIF), obeys a differential equation which can be approximated using discrete ti... | SP:ef7cf9c0569adc304bdc8601229ac7579178a871 |
Efficient Probabilistic Logic Reasoning with Graph Neural Networks | 1 INTRODUCTION . Knowledge graphs collect and organize relations and attributes about entities , which are playing an increasingly important role in many applications , including question answering and information retrieval . Since knowledge graphs may contain incorrect , incomplete or duplicated records , additional p... | This paper proposes a framework for solving the probabilistic logic reasoning problem by integrating Markov neural networks and graph neural networks to combine their individual features into a more expressive and scalable framework. Graph neural networks are used for learning representations for Knowledge graphs and a... | SP:18ddfe1194f8f8c208068c83157cb7935b962c7a |
Efficient Probabilistic Logic Reasoning with Graph Neural Networks | 1 INTRODUCTION . Knowledge graphs collect and organize relations and attributes about entities , which are playing an increasingly important role in many applications , including question answering and information retrieval . Since knowledge graphs may contain incorrect , incomplete or duplicated records , additional p... | The paper proposes to use graph neural networks (GNN) for inference in MLN. The main motivation seems to be that inference in traditional MLN is computationally inefficient. The paper is cryptic about precisely why this is the case. There is some allusion in the introduction as to grounding being exponential in the num... | SP:18ddfe1194f8f8c208068c83157cb7935b962c7a |
Disentangled Cumulants Help Successor Representations Transfer to New Tasks | 1 INTRODUCTION . Natural intelligence is able to solve many diverse tasks by transferring knowledge and skills from one task to another . For example , by knowing about objects and how to move them in 3D space , it is possible to learn how to sort them by shape or colour faster . However , many of the current state-of-... | The paper addresses the problem of policy transfer in reinforcement learning, which is an extremely relevant open problem in RL, and is being actively studied by the community.
The authors propose a framework for discovering a set of policies without external supervision which can then be used to produce reasonable pe... | SP:31a497e4a1c74532ad3357b19e9fa4000db61115 |
Disentangled Cumulants Help Successor Representations Transfer to New Tasks | 1 INTRODUCTION . Natural intelligence is able to solve many diverse tasks by transferring knowledge and skills from one task to another . For example , by knowing about objects and how to move them in 3D space , it is possible to learn how to sort them by shape or colour faster . However , many of the current state-of-... | This paper proposes to pre-train policies on some goal-reaching tasks, and then leverage the associated successor features to improve the learning of a new task. The method heavily draws from the Generalized Policy Evaluation/Improvement framework without adding much to it. The only relevant point would be showing (as ... | SP:31a497e4a1c74532ad3357b19e9fa4000db61115 |
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) is typically concerned with finding an optimal policy that maximises expected return for a given Markov decision process ( MDP ) with an unknown reward and transition function . If these were known , the optimal policy could in theory be computed without environment intera... | This paper considers a version of reinforcement learning problem where an unknown prior distribution over Markov decision processes are assumed and the learner can sample from it. After sampling a MDP, a standard reinforcement learning is done. Then the paper investigates the Bayes-optimal strategy for such meta-learni... | SP:daf8080733b61b118faad8dca6f09691ecaa3005 |
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) is typically concerned with finding an optimal policy that maximises expected return for a given Markov decision process ( MDP ) with an unknown reward and transition function . If these were known , the optimal policy could in theory be computed without environment intera... | This paper presents a new deep reinforcement learning method that can efficiently trade-off exploration and exploitation. An optimal policy for this trade-off can be solved under the Bayesian-adaptive MDP framework, but in practice, the computation is often intractable. To solve the challenge and approximate a Bayesian... | SP:daf8080733b61b118faad8dca6f09691ecaa3005 |
DSReg: Using Distant Supervision as a Regularizer | 1 INTRODUCTION . Consider the following sentences in a text-classification task , in which we want to identify text describing hotels with good service/staff ( as depicted as aspect-level sentiment classification in Tang et al . ( 2015 ) ; Li et al . ( 2016 ) ; Lei et al . ( 2016 ) ) : • S1 : the staff are great . ( po... | This paper proposes to improve performance of NLP tasks by focusing on negative examples that are similar to positive examples (e.g. hard negatives). This is achieved by regularizing the model using extra output classifiers trained to classify examples into up to three classes: positive, negative-easy, and negative-har... | SP:08350406f571056b5652cff2e4b0c5ed7ccb13c8 |
DSReg: Using Distant Supervision as a Regularizer | 1 INTRODUCTION . Consider the following sentences in a text-classification task , in which we want to identify text describing hotels with good service/staff ( as depicted as aspect-level sentiment classification in Tang et al . ( 2015 ) ; Li et al . ( 2016 ) ; Lei et al . ( 2016 ) ) : • S1 : the staff are great . ( po... | This paper is aimed at tackling a general issue in NLP: Hard-negative training data (negative but very similar to positive) can easily confuse standard NLP model. To solve this problem, the authors first applied distant supervision technique to harvest hard-negative training examples and then transform the original tas... | SP:08350406f571056b5652cff2e4b0c5ed7ccb13c8 |
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking | 1 INTRODUCTION . Since the first Adversarial Example ( AE ) against traffic sign image classification discovered by Eykholt et al . ( Eykholt et al. , 2018 ) , several research work in adversarial machine learning ( Eykholt et al. , 2017 ; Xie et al. , 2017 ; Lu et al. , 2017a ; b ; Zhao et al. , 2018b ; Chen et al. , ... | The paper addresses adversarial attacks against visual perception pipelines in autonomous driving. Both subprocesses in the visual perception pipeline, object detection and multiple object tracking (MOT), are considered. The paper proposes a novel approach in adversarial attacks, the tracking hijacking, which can fool ... | SP:c64e935f86a415a464632de66ffe1d610df585e4 |
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking | 1 INTRODUCTION . Since the first Adversarial Example ( AE ) against traffic sign image classification discovered by Eykholt et al . ( Eykholt et al. , 2018 ) , several research work in adversarial machine learning ( Eykholt et al. , 2017 ; Xie et al. , 2017 ; Lu et al. , 2017a ; b ; Zhao et al. , 2018b ; Chen et al. , ... | This paper is about conducting evasion attacks against Multiple Object Tracking (MOT) techniques. Compared to existing work on adversarial examples against object detection, to attack MOT techniques, the adversary needs to successfully fool multiple frames, and the authors show that by naively using existing attack app... | SP:c64e935f86a415a464632de66ffe1d610df585e4 |
Evaluating Lossy Compression Rates of Deep Generative Models | Deep generative models have achieved remarkable progress in recent years . Despite this progress , quantitative evaluation and comparison of generative models remains as one of the important challenges . One of the most popular metrics for evaluating generative models is the log-likelihood . While the direct computatio... | This paper presents a method for evaluating latent-variable generative models in terms of the rate-distortion curve that compares the number of bits needed to encode the representation with how well you can reconstruct an input under some distortion measure. To estimate this curve, the author’s use AIS and show how int... | SP:950c845314ba6a65208f78be3e42b47d79befd7f |
Evaluating Lossy Compression Rates of Deep Generative Models | Deep generative models have achieved remarkable progress in recent years . Despite this progress , quantitative evaluation and comparison of generative models remains as one of the important challenges . One of the most popular metrics for evaluating generative models is the log-likelihood . While the direct computatio... | The paper proposes a new way to evaluate generative models that don't have tractable likelihoods, such as VAEs or GANs. Such generative models are composed of a prior over latent variables and a decoder that maps latent variables to data. The idea is to evaluate a trained model in terms of the best (lossy) compression ... | SP:950c845314ba6a65208f78be3e42b47d79befd7f |
On the Dynamics and Convergence of Weight Normalization for Training Neural Networks | 1 INTRODUCTION . Dynamic normalization in neural networks is a re-parametrization procedure between the layers that improves stability during training and leads to faster convergence . This approach was popularized with the introduction of Batch Normalization ( BatchNorm ) in [ 20 ] and has led to a plethora of additio... | This paper presents a general proof of the convergence of two-layer ReLU networks with weight normalization trained with gradient descent. Weight normalization re-parameterizes the weights to decouple the directions and lengths of kernels. Depending on the lengths of kernels the training process can be divided into two... | SP:24573aabc247456e2c8f00de434d586a8c18fb26 |
On the Dynamics and Convergence of Weight Normalization for Training Neural Networks | 1 INTRODUCTION . Dynamic normalization in neural networks is a re-parametrization procedure between the layers that improves stability during training and leads to faster convergence . This approach was popularized with the introduction of Batch Normalization ( BatchNorm ) in [ 20 ] and has led to a plethora of additio... | Global convergence of NNs is an important research direction in deep learning. There have been significant progresses in this direction since last year. Most noticeable, the Neural tangent kernels (NTK) [1], which shows in the infinite width setting, NTK is deterministic and remains almost constant during gradient desc... | SP:24573aabc247456e2c8f00de434d586a8c18fb26 |
DIME: AN INFORMATION-THEORETIC DIFFICULTY MEASURE FOR AI DATASETS | 1 INTRODUCTION . Empirical machine learning research relies heavily on comparing performance of algorithms on a few standard benchmark datasets . Moreover , researchers frequently introduce new datasets that they believe to be more challenging than existing benchmarks . However , we lack objective measures of dataset d... | The paper proposes a measure of difficulty for datasets. Prior work in this space has often utilized certain indicators like the overlap of samples across different classes etc. [A] While this work defines a model-agnostic error as the measure of difficulty, which should encompass all possible indicators of error. Then... | SP:dbb2e549f21492129fac9e6944485440cfe093e0 |
DIME: AN INFORMATION-THEORETIC DIFFICULTY MEASURE FOR AI DATASETS | 1 INTRODUCTION . Empirical machine learning research relies heavily on comparing performance of algorithms on a few standard benchmark datasets . Moreover , researchers frequently introduce new datasets that they believe to be more challenging than existing benchmarks . However , we lack objective measures of dataset d... | So this paper is interesting. It's sort of pursuing a similar path as recent works that use neural networks to evaluate (e.g., inception score, FID), notably those that optimize some lower bound of an information measure (e.g., MINE). In this case, the setting is "datasets", and the thing they are trying to quantify is... | SP:dbb2e549f21492129fac9e6944485440cfe093e0 |
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled embedding of n-gram statistics | 1 INTRODUCTION . Recent work ( Strubell et al. , 2019 ) has brought significant attention by demonstrating potential cost and environmental impact of developing and training state-of-the-art models for Natural Language Processing ( NLP ) tasks . The work suggested several countermeasures for changing the situation . On... | This paper introduces a technique to project n gram statistic vectors into a lower dimensional space in order to improve memory efficiency and lower training time. The paper is motivated by the important problem of trying to improve efficiency of existing language models which can be extremely resource intensive. The a... | SP:20b1c29036733dee134d3dfa245e574aa82b7d3d |
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled embedding of n-gram statistics | 1 INTRODUCTION . Recent work ( Strubell et al. , 2019 ) has brought significant attention by demonstrating potential cost and environmental impact of developing and training state-of-the-art models for Natural Language Processing ( NLP ) tasks . The work suggested several countermeasures for changing the situation . On... | This paper proposes the use of hyperdimensional (HD) vectors to represent n-gram statistics. The HD vectors are first generated from the whole corpus. Then, it is aggregated or bundled to a vector for each sample as an input of a classifier training. The evaluation is conducted on four datasets: Chatbot, AskUbuntu, Web... | SP:20b1c29036733dee134d3dfa245e574aa82b7d3d |
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs | Face completion is a challenging conditional image synthesis task . This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks ( GANs ) progressively from low resolution to high resolution . We present structure-aware and frequency-oriented at... | This paper proposes a face completion network that synthesizes the missing part in the face images with GANs. Using facial landmarks and facial attributes, face completion became controllable as both are used as conditional information (input) for the generation (synthesis). Moreover, the proposed Frequency-Oriented At... | SP:fc9bd3cf5e1fc8affc1e1d1b183eb4bdd92ddf1d |
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs | Face completion is a challenging conditional image synthesis task . This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks ( GANs ) progressively from low resolution to high resolution . We present structure-aware and frequency-oriented at... | This paper proposes controllable and interpretable high-resolution and fast face completion by learning generative adversarial networks (GANs) progressively from low resolution to high resolution. It combines the masks, landmarks, corrupted images as inputs to generate completed images in high-resolution. The proposed ... | SP:fc9bd3cf5e1fc8affc1e1d1b183eb4bdd92ddf1d |
CLAREL: classification via retrieval loss for zero-shot learning | 1 INTRODUCTION . Deep learning-based approaches have demonstrated superior flexibility and generalization capabilities in information processing on a wide variety of tasks , such as vision , speech and language ( LeCun et al. , 2015 ) . However , it has been widely realized that the transfer of deep representations to ... | This paper tackles zero-shot and generalised zero-shot learning by using the per-image semantic information. An instance-based loss is introduced to align images and their corresponding text in the same embedding space. To solve the extreme imbalanced issue of generalized zero-shot learning, the authors propose to scal... | SP:9824d6fb46e2ad9c55ae3e55171792ad83c14d8b |
CLAREL: classification via retrieval loss for zero-shot learning | 1 INTRODUCTION . Deep learning-based approaches have demonstrated superior flexibility and generalization capabilities in information processing on a wide variety of tasks , such as vision , speech and language ( LeCun et al. , 2015 ) . However , it has been widely realized that the transfer of deep representations to ... | The paper proposes to to use four different losses to train a joint text-image embedding space for zero-shot learning. The four losses consist of a classification loss given text descriptions, a classification loss given images, two contrastive losses given pairs of text and images. The paper also discusses how to bala... | SP:9824d6fb46e2ad9c55ae3e55171792ad83c14d8b |
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation | 1 INTRODUCTION . Learning from graph-structured data has seen explosive growth over the last few years , as graphs are a convenient formalism to model the broad class of data that has objects ( treated as vertices ) with some known relationships ( treated as edges ) . Example usages include reasoning about physical and... | This paper introduces a new type of Graph Neural Network (GNN) that incorporates Feature-wise Linear Modulation (FiLM) layers. Current GNNs update the target representations by aggregating information from neighbouring nodes without taking into the account the target node representation. As graph networks might benefit... | SP:19147e08c4a7343bee155f8e74362d8214bb35e1 |
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation | 1 INTRODUCTION . Learning from graph-structured data has seen explosive growth over the last few years , as graphs are a convenient formalism to model the broad class of data that has objects ( treated as vertices ) with some known relationships ( treated as edges ) . Example usages include reasoning about physical and... | The paper proposes a new Graph Neural Network (GNN) architecture that uses Feature-wise Linear Modulation (FiLM) to condition the source-to-target node message-passing based on the target node representation. In this way, GNN-FiLM aims to allow a GNN's message propagation to "focus on feature that are especially releva... | SP:19147e08c4a7343bee155f8e74362d8214bb35e1 |
Universal Safeguarded Learned Convex Optimization with Guaranteed Convergence | 1 INTRODUCTION . Solving scientific computing problems often requires application of efficient and scalable optimization algorithms . Despite the ever improving rates of convergence of state-of-the-art general purpose algorithms , their ability to apply to real-time applications is still limited due to the relatively l... | This paper proposes a framework to unfold the safeguarded Krasnosel’ski˘ı-Mann (SKM) method for the learn to optimization (L2O) schemes. First, SKM is proposed in Algorithm 1 with convergence guarantee established in Theorem 3.1 and Corollary 3.1. Then, SKM is unfolded and executed with a neural network summarized in A... | SP:62366ea14ace4437298fb9ddf7f095563709e3bf |
Universal Safeguarded Learned Convex Optimization with Guaranteed Convergence | 1 INTRODUCTION . Solving scientific computing problems often requires application of efficient and scalable optimization algorithms . Despite the ever improving rates of convergence of state-of-the-art general purpose algorithms , their ability to apply to real-time applications is still limited due to the relatively l... | This paper presents a unified framework for parametrizing provably convergent algorithms and learning the parameters for a training dataset of problem instances of interest. The learned algorithm can then be used on unseen problems. One key idea to this algorithm is that it is safeguarded, meaning it will perform some ... | SP:62366ea14ace4437298fb9ddf7f095563709e3bf |
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning | 1 INTRODUCTION . Meta-learning or learning to learn is an appealing notion due to its potential in addressing important challenges when applying machine learning to real-world problems . In particular , learning from prior tasks but being able to to adapt quickly to new tasks improves learning efficiency , model robust... | This paper analyzes the popular MAML (Model-Agnostic Meta-Learner) method, and thereafter proposes a new approach to meta-learning based on observations from empirical studies. The key idea of the work is to separate the base model and task-specific adaptation components of MAML. This decoupling of adaptation and model... | SP:62c41894b5a79ff20a4a1e3d56c646e08981814d |
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning | 1 INTRODUCTION . Meta-learning or learning to learn is an appealing notion due to its potential in addressing important challenges when applying machine learning to real-world problems . In particular , learning from prior tasks but being able to to adapt quickly to new tasks improves learning efficiency , model robust... | This paper presents an experimental study of gradient based meta learning models and most notably MAML. The results suggest that modeling and adaptation are happening on different parts of the network leading to an inefficient use of the model capacity which explains the poor performance of MAML on linear (or small net... | SP:62c41894b5a79ff20a4a1e3d56c646e08981814d |
Improving the Gating Mechanism of Recurrent Neural Networks | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) have become a standard machine learning tool for learning from sequential data . However , RNNs are prone to the vanishing gradient problem , which occurs when the gradients of the recurrent weights become vanishingly small as they get backpropagated through time ( Ho... | This paper introduces two novel techniques to help long term signal propagation in RNNs. One is an initialization strategy which uses inverse sigmoid function to avoid the decay of the contribution of the input earlier in time and another is a new design of a refine gate which pushes the value of the gate closer to 0 o... | SP:0a036636575bd445f18928c438a9fb063f11b012 |
Improving the Gating Mechanism of Recurrent Neural Networks | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) have become a standard machine learning tool for learning from sequential data . However , RNNs are prone to the vanishing gradient problem , which occurs when the gradients of the recurrent weights become vanishingly small as they get backpropagated through time ( Ho... | This paper proposes to improve the learnability of the gating mechanism in RNN by two modifications on the standard RNN structure, uniform gate initialization and refine gate. The authors give some propositions to show that the refine gate can maintain an effective forget effect within a larger range of timescale. The ... | SP:0a036636575bd445f18928c438a9fb063f11b012 |
Regularizing Deep Multi-Task Networks using Orthogonal Gradients | 1 INTRODUCTION . Deep neural networks have proven to be very successful at solving isolated tasks in a variety of fields ranging from computer vision to NLP . In contrast to this single task setup , multi-task learning aims to train one model on several problems simultaneously . This approach would incentivize it to tr... | This paper embraces the idea that better multi-task/lifelong learning can be achieved if tasks produce gradients that are orthogonal to the gradients produced by other tasks. The authors propose an approach to regularizing learning in order to incentivize this to happen. However, as they mention themselves, the regular... | SP:3aa14d5bb77c3cdf165b832dcc81f8b7867cefe6 |
Regularizing Deep Multi-Task Networks using Orthogonal Gradients | 1 INTRODUCTION . Deep neural networks have proven to be very successful at solving isolated tasks in a variety of fields ranging from computer vision to NLP . In contrast to this single task setup , multi-task learning aims to train one model on several problems simultaneously . This approach would incentivize it to tr... | In this paper, the author analyzed gradient regularization in deep multitask learning. They empirically discovered a sharper concentration (low variance) in angles between the task gradient distributions could potentially improve the performance in multi-task learning. Then they proposed a new gradient regularization t... | SP:3aa14d5bb77c3cdf165b832dcc81f8b7867cefe6 |
Smoothness and Stability in GANs | 1 INTRODUCTION : TAMING INSTABILITY WITH SMOOTHNESS . Generative adversarial networks ( Goodfellow et al. , 2014 ) , or GANs , are a powerful class of generative models defined through minimax game . GANs and their variants have shown impressive performance in synthesizing various types of datasets , especially natural... | This paper provides a unified theoretical framework for regularizing GAN losses. It accounts for most regularization technics especially spectral normalization and gradient penalty and explains how those two methods are in fact complementary. So far this was only observed experimentally but without any theoretical insi... | SP:f467d9904b9633d00e56dbc297caae6a21208b18 |
Smoothness and Stability in GANs | 1 INTRODUCTION : TAMING INSTABILITY WITH SMOOTHNESS . Generative adversarial networks ( Goodfellow et al. , 2014 ) , or GANs , are a powerful class of generative models defined through minimax game . GANs and their variants have shown impressive performance in synthesizing various types of datasets , especially natural... | The work studies the relationship between the stability and the smoothness of GANs based on the proposition which was proposed by Bertsekas . It explains many nontrivial empirical observations when one is training GANs, including both of the necessities of the spectral normalization and the gradient penalty, in a theor... | SP:f467d9904b9633d00e56dbc297caae6a21208b18 |
Off-Policy Actor-Critic with Shared Experience Replay | 1 INTRODUCTION . Value-based and actor-critic policy gradient methods are the two leading techniques of constructing general and scalable reinforcement learning agents ( Sutton et al. , 2018 ) . Both have been combined with non-linear function approximation ( Tesauro , 1995 ; Williams , 1992 ) , and have achieved remar... | This paper aims to improve the efficiency of the actor-critic method. The authors first analyzed the cause of instability in the prior work, from the perspective of bias and variance. Two remedies were then presented: (i) mixing the experience replay with online learning; (ii) proposing a trust region scheme to select ... | SP:e1317ed002e3e0f08ba90506cb2c38d65265a102 |
Off-Policy Actor-Critic with Shared Experience Replay | 1 INTRODUCTION . Value-based and actor-critic policy gradient methods are the two leading techniques of constructing general and scalable reinforcement learning agents ( Sutton et al. , 2018 ) . Both have been combined with non-linear function approximation ( Tesauro , 1995 ; Williams , 1992 ) , and have achieved remar... | This paper investigates off-policy actor critic (AC) learning with experience replay using V-trace. It shows that V-trace policy gradient is not guaranteed to converge to a local optimal solution. To mitigate the bias and variance problem of V-trace and importance sampling, a trust region approach is proposed to adapti... | SP:e1317ed002e3e0f08ba90506cb2c38d65265a102 |
State-only Imitation with Transition Dynamics Mismatch | 1 INTRODUCTION . In the Reinforcement Learning ( RL ) framework , the objective is to train policies that maximize a certain reward criterion . Deep-RL , which combines RL with the recent advances in the field of deeplearning , has produced algorithms demonstrating remarkable success in areas such as games ( Mnih et al... | The manuscript considers the problem of imitation learning when the system dynamics of the agent are different from the dynamics of the expert. The paper proposes Indirect Imitation Learning (I2L), which aims to perform imitation learning with respect to a trajectory buffer that contains some of the previous trajectori... | SP:001e57e71bafdb52d6511bdd6aa73b78d60248f2 |
State-only Imitation with Transition Dynamics Mismatch | 1 INTRODUCTION . In the Reinforcement Learning ( RL ) framework , the objective is to train policies that maximize a certain reward criterion . Deep-RL , which combines RL with the recent advances in the field of deeplearning , has produced algorithms demonstrating remarkable success in areas such as games ( Mnih et al... | The paper proposes an imitation method, I2L, that learns from state-only demonstrations generated in an expert MDP that may have different transition dynamics than the agent MDP. I2L modifies the existing adversarial inverse RL algorithm: instead of training the disciminator to distinguish demonstrations vs. samples, I... | SP:001e57e71bafdb52d6511bdd6aa73b78d60248f2 |
Meta-Q-Learning | M E TA - Q - L E A R N I N G Rasool Fakoor1 , Pratik Chaudhari2∗ , Stefano Soatto1 , Alexander Smola1 1 Amazon Web Services 2 University of Pennsylvania Email : { fakoor , soattos , smola } @ amazon.com , pratikac @ seas.upenn.edu A B S T R A C T This paper introduces Meta-Q-Learning ( MQL ) , a new off-policy algorith... | The authors propose meta Q-learning, an algorithm for off-policy meta RL. The idea is to meta-train a context-dependent policy to maximize the expected return averaged over all training tasks, and then adapt this policy to any new task by leveraging both novel and past experience using importance sampling corrections. ... | SP:39e2b5a77cf6a3cf90efd0e78b2041855c4139fa |
Meta-Q-Learning | M E TA - Q - L E A R N I N G Rasool Fakoor1 , Pratik Chaudhari2∗ , Stefano Soatto1 , Alexander Smola1 1 Amazon Web Services 2 University of Pennsylvania Email : { fakoor , soattos , smola } @ amazon.com , pratikac @ seas.upenn.edu A B S T R A C T This paper introduces Meta-Q-Learning ( MQL ) , a new off-policy algorith... | This paper proposes Meta Q-Learning (MQL), an algorithm for efficient off-policy meta-learning. The method relies on a simple multi-task objective which provides initial parameter values for the adaptation phase. Adaptation is performed by gradient descent, minimizing TD-error on the new validation task (regularizing t... | SP:39e2b5a77cf6a3cf90efd0e78b2041855c4139fa |
Discriminability Distillation in Group Representation Learning | 1 INTRODUCTION . With the rapid development of deep learning and the easy access to large-scale group data , recognition tasks using group information have drawn great attention in the computer vision community . The rich information provided by different elements can complement each other to boost the performance of t... | This paper studies how to aggregate features from group inputs. The paper proposes Discriminability Distillation Learning (DDL) to compute the aggregation coefficients. The method assumes that each sample has a discriminability property that is directly related to the task. The authors define this property and propose... | SP:97138888ff5e94c0c460690fd21246ab1bf5a39b |
Discriminability Distillation in Group Representation Learning | 1 INTRODUCTION . With the rapid development of deep learning and the easy access to large-scale group data , recognition tasks using group information have drawn great attention in the computer vision community . The rich information provided by different elements can complement each other to boost the performance of t... | In this paper, the authors proposed a discriminability distillation learning (DDL) method for the group representation learning, such as action recognition recognition and face recognition. The main insight of DDL is to explicitly design the discrimiability using embedded class centroids on a proxy set, and show the di... | SP:97138888ff5e94c0c460690fd21246ab1bf5a39b |
Learning to Prove Theorems by Learning to Generate Theorems | 1 INTRODUCTION . Automated theorem proving is a key task in Artificial Intelligence . The goal is to automatically generate a proof , given a conjecture ( the target theorem ) and a knowledge base of known facts , all expressed in a formal language . Automated theorem proving is useful in a wide range of applications ,... | This paper proposes a generative model for proofs in Metamath, a language for formalizing mathematics. The model includes neural networks, which provide guidance about which fact to try to prove next and how to prove the fact from the facts derived so far. The parameters of these networks are learned from existing proo... | SP:ef00d1cc6981df5591b757e7a46ba9179d1fc50a |
Learning to Prove Theorems by Learning to Generate Theorems | 1 INTRODUCTION . Automated theorem proving is a key task in Artificial Intelligence . The goal is to automatically generate a proof , given a conjecture ( the target theorem ) and a knowledge base of known facts , all expressed in a formal language . Automated theorem proving is useful in a wide range of applications ,... | This paper focuses on the problem of developing deep learning systems that can prove theorems in a mathematical formalism -- in this case, MetaMath. This has been a rapidly growing topic in the past few years, as evidenced by the numerous cited works. What sets this work apart from others is its focus on the instrument... | SP:ef00d1cc6981df5591b757e7a46ba9179d1fc50a |
Collapsed amortized variational inference for switching nonlinear dynamical systems | 1 INTRODUCTION . Consider watching from above an airplane flying across country or a car driving through a field . The vehicle ’ s motion is composed of straight , linear dynamics and curving , nonlinear dynamics . This is illustrated in fig . 1 ( a ) . In this paper , we propose a new inference algorithm for fitting s... | In this paper, the authors consider the problem of learning model parameters of a switching nonlinear dynamical system from a dataset. They propose a new variational inference algorithm for this model-learning problem that marginalizes all discrete random variables in the model using the forward-backward algorithm and,... | SP:85cc769dc87f910a4aff638f833764ebbac63418 |
Collapsed amortized variational inference for switching nonlinear dynamical systems | 1 INTRODUCTION . Consider watching from above an airplane flying across country or a car driving through a field . The vehicle ’ s motion is composed of straight , linear dynamics and curving , nonlinear dynamics . This is illustrated in fig . 1 ( a ) . In this paper , we propose a new inference algorithm for fitting s... | This paper proposes a method to segment time series into discrete intervals in an unsupervised way. The data is modeled using a state space model where each state consists of a discrete and a continuous part. The discrete state denotes the segment the system is currently in and the continuous state which is conditioned... | SP:85cc769dc87f910a4aff638f833764ebbac63418 |
Pruned Graph Scattering Transforms | 1 INTRODUCTION . The abundance of graph-structured data calls for advanced learning techniques , and complements nicely standard machine learning tools that can not be directly applied to irregular data domains . Permeating the benefits of deep learning to the graph domain , graph convolutional networks ( GCNs ) provid... | A scattering transform on graphs consists in the cascade of wavelets, modulus non-linearity and a low-pass filter. The wavelets and the low-pass are designed in the spectral domain, which is computationally extensive. Instead to compute any cascades of wavelets, this paper proposes to prune scattering paths which have ... | SP:1ccaa054dc814a12e6cea27fdc8cdd0d53b25794 |
Pruned Graph Scattering Transforms | 1 INTRODUCTION . The abundance of graph-structured data calls for advanced learning techniques , and complements nicely standard machine learning tools that can not be directly applied to irregular data domains . Permeating the benefits of deep learning to the graph domain , graph convolutional networks ( GCNs ) provid... | In this paper, the authors developed graph scattering transforms (GST) with a pruning algorithm, with the aim to reduce the running time and space cost, improve robustness to perturbations on input graph signal, and encourage flexibility for domain adaption. To this end, pruned graph scattering transform (pGST) was pro... | SP:1ccaa054dc814a12e6cea27fdc8cdd0d53b25794 |
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels | We propose using active learning based techniques to further improve the stateof-the-art semi-supervised learning MixMatch algorithm . We provide a thorough empirical evaluation of several active-learning and baseline methods , which successfully demonstrate a significant improvement on the benchmark CIFAR-10 , CIFAR-1... | This paper proposes a method that can deal with an active-learning scenario for the recently proposed semi-supervised learning method: MixMatch. More specifically, the proposed method considers uncertainty measures to choose samples and a diversification step to ensure diversity within the sampled batch. For uncertai... | SP:19e84ea0dc79d30cfbdb25c0b768536f38820885 |
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels | We propose using active learning based techniques to further improve the stateof-the-art semi-supervised learning MixMatch algorithm . We provide a thorough empirical evaluation of several active-learning and baseline methods , which successfully demonstrate a significant improvement on the benchmark CIFAR-10 , CIFAR-1... | The paper proposes to combine active learning techniques with MixMatch for semi-supervised learning. First, they review active learning and semi-supervised learning, especially MixMatch. Instead of traditional semi-supervised learning with a fixed set of labeled examples, they incrementally grow the labeled set as the ... | SP:19e84ea0dc79d30cfbdb25c0b768536f38820885 |
MMA Training: Direct Input Space Margin Maximization through Adversarial Training | 1 INTRODUCTION Despite their impressive performance on various learning tasks , neural networks have been shown to be vulnerable to adversarial perturbations ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . An artificially constructed imperceptible perturbation can cause a significant drop in the prediction accuracy ... | This paper proposes an adaptive margin-based adversarial training (eg. MMA) approach to train robust DNNs by maximizing the shortest margin of inputs to the decision boundary. Theoretical analyses have been provided to understand the connection between robust optimization and margin maximization. The main difference be... | SP:75d17035de7c88ebb45e60795d3acd8f0e93b84b |
MMA Training: Direct Input Space Margin Maximization through Adversarial Training | 1 INTRODUCTION Despite their impressive performance on various learning tasks , neural networks have been shown to be vulnerable to adversarial perturbations ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . An artificially constructed imperceptible perturbation can cause a significant drop in the prediction accuracy ... | This paper proposes a method, Max-Margin Adversarial (MMA) training, for robust learning against adversarial attacks. In the MMA, the margin in the input space is directly maximized. In order to alleviate an instability of the learning, a softmax variant of the max-margin is introduced. Moreover, the margin-maximizatio... | SP:75d17035de7c88ebb45e60795d3acd8f0e93b84b |
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers | 1 INTRODUCTION . Despite the impressive success that deep neural networks have achieved in a wide range of challenging tasks , the inference in deep neural networks is highly memory-intensive and computationintensive due to the over-parameterization of deep neural networks . Network pruning ( LeCun et al . ( 1990 ) ; H... | This paper presents a novel network pruning algorithm -- Dynamic Sparse Training. It aims at jointly finding the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. The experiments on MNIST, and cifar-10 show that proposed model can find sparse ... | SP:24243429012ab70e9638a009b78e5a9a5b8d73be |
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers | 1 INTRODUCTION . Despite the impressive success that deep neural networks have achieved in a wide range of challenging tasks , the inference in deep neural networks is highly memory-intensive and computationintensive due to the over-parameterization of deep neural networks . Network pruning ( LeCun et al . ( 1990 ) ; H... | This paper proposes an algorithm for training networks with sparse parameter tensors. This involves achieving sparsity by application of a binary mask, where the mask is determined by current parameter values and a learned threshold. It also involves the addition of a specific regularizer which encourages the threshold... | SP:24243429012ab70e9638a009b78e5a9a5b8d73be |
NORML: Nodal Optimization for Recurrent Meta-Learning | 1 INTRODUCTION . Humans have a remarkable capability to learn useful concepts from a small number of examples or a limited amount of experience . In contrast most machine learning methods require large , labelled datasets to learn effectively . Little is understood about the actual learning algorithm ( s ) used by the ... | This submission proposes NORML, a meta-learning method that 1) learns initial parameters for a base model that leads to good few-shot learning performance and 2) where a recurrent neural network (LSTM) is used to control the learning updates on a small support set for a given task. The method is derived specifically fo... | SP:f66b8d030e2dfef6e9c4fc7b35abd996d957a3fc |
NORML: Nodal Optimization for Recurrent Meta-Learning | 1 INTRODUCTION . Humans have a remarkable capability to learn useful concepts from a small number of examples or a limited amount of experience . In contrast most machine learning methods require large , labelled datasets to learn effectively . Little is understood about the actual learning algorithm ( s ) used by the ... | This paper proposes a meta-learner that learns how to make parameter updates for a model on a new few-shot learning task. The proposed meta-learner is an LSTM that proposes at each time-step, a point-wise multiplier for the gradient of the hidden units and for the hidden units of the learner neural network, which are t... | SP:f66b8d030e2dfef6e9c4fc7b35abd996d957a3fc |
Learning Latent Representations for Inverse Dynamics using Generalized Experiences | 1 INTRODUCTION . In reinforcement learning ( RL ) , an agent optimizes its behaviour to maximize a specific reward function that encodes tasks such as moving forward or reaching a target . After training , the agent simply executes the learned policy from its initial state until termination . In practical settings in r... | This paper proposes a method to learn locomotion and navigation to a goal location or through a set of waypoints for simulated legged robots. The contributions of this paper include 1) generalized experience, which is a data-augmentation technique to add more orientation-invariant experience, and 2) a latent representa... | SP:006c2334ad366e0c4558bcfccdd89f993fb8bba7 |
Learning Latent Representations for Inverse Dynamics using Generalized Experiences | 1 INTRODUCTION . In reinforcement learning ( RL ) , an agent optimizes its behaviour to maximize a specific reward function that encodes tasks such as moving forward or reaching a target . After training , the agent simply executes the learned policy from its initial state until termination . In practical settings in r... | The paper proposes a method for exploiting structure in locomotive tasks for efficiently learning low-level control policies that pass through waypoints while achieving some goal (typically 3D Cartesian position). This is in contrast to goal-conditioned RL policies that sample random goals during training and are thus ... | SP:006c2334ad366e0c4558bcfccdd89f993fb8bba7 |
Learning to Learn Kernels with Variational Random Features | 1 INTRODUCTION . Humans have the instinct to effortlessly learn new concepts from a few examples and show great generalization ability to new samples . However , existing machine learning models , e.g. , deep neural networks ( DNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016a ) , rely highly on large-scale annotate... | This paper proposes a meta-learning framework for learning adaptive kernels using a meta-learner. For representing kernels, the paper learns a variational posterior for the kernel features, by maximizing the Evidence lower Bound. Furthermore, to plug the kernel learning into the meta-learning framework, they let the va... | SP:dce8715440bee1c1dbf8fabc4f0c85bd5d7ddf1f |
Learning to Learn Kernels with Variational Random Features | 1 INTRODUCTION . Humans have the instinct to effortlessly learn new concepts from a few examples and show great generalization ability to new samples . However , existing machine learning models , e.g. , deep neural networks ( DNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016a ) , rely highly on large-scale annotate... | This paper studies meta-learning problem with few-shot learning settings. The author proposes a learn each task predictive function via the form of random Fourier features, where the kernel is jointly learned from all tasks. The novel part is the parametrization of inference network using LSTM such that the random feat... | SP:dce8715440bee1c1dbf8fabc4f0c85bd5d7ddf1f |
Simple is Better: Training an End-to-end Contract Bridge Bidding Agent without Human Knowledge | 1 INTRODUCTION . Games have long been recognized as a testbed for reinforcement learning . Recent technology advancements have outperformed top level experts in perfect information games like Chess ( Campbell et al. , 2002 ) and Go ( Silver et al. , 2016 ; 2017 ) , through human supervision and selfplay . During recent... | The authors propose a deep learning agent for automatic bidding in the bridge game. The agent is trained with a standard A3C reinforcement learning model with self-play, and the internal neural network only takes a rather succinct representation of the bidding history as the input. Experiment results demonstrate state-... | SP:e5435a2d586d6f2bebf436aae8a7fb3602064ab8 |
Simple is Better: Training an End-to-end Contract Bridge Bidding Agent without Human Knowledge | 1 INTRODUCTION . Games have long been recognized as a testbed for reinforcement learning . Recent technology advancements have outperformed top level experts in perfect information games like Chess ( Campbell et al. , 2002 ) and Go ( Silver et al. , 2016 ; 2017 ) , through human supervision and selfplay . During recent... | This paper develops a method to train agents to bid competitively in the game of Bridge. The authors focus on the bidding phase of the game and develop a model to predict the best bid to make at each turn of the phase. The difficulty in the bidding lies in understanding the signals provided by your own teammate as well... | SP:e5435a2d586d6f2bebf436aae8a7fb3602064ab8 |
Selection via Proxy: Efficient Data Selection for Deep Learning | 1 INTRODUCTION . Data selection methods , such as active learning and core-set selection , improve the data efficiency of machine learning by identifying the most informative training examples . To quantify informativeness , these methods depend on semantically meaningful features or a trained model to calculate uncert... | The paper proposes a method for selecting a subset of a large dataset to reduce the computational costs of deep neural netwoks. The main idea is to train a proxy model, a smaller version of the full neural network, to choose important data points for active learning or core-set selection. Experiments on standard classi... | SP:fa4272fd8c8acea21a01d8fd6542a51534c1aee8 |
Selection via Proxy: Efficient Data Selection for Deep Learning | 1 INTRODUCTION . Data selection methods , such as active learning and core-set selection , improve the data efficiency of machine learning by identifying the most informative training examples . To quantify informativeness , these methods depend on semantically meaningful features or a trained model to calculate uncert... | This paper presents a method to speed up the data selection in active learning and core-set learning. The authors present a simple idea: instead of using the full model to select data points, they use a smaller model with fewer layers, potentially trained for fewer iterations. The authors show that this simple approach... | SP:fa4272fd8c8acea21a01d8fd6542a51534c1aee8 |
Mutual Information Gradient Estimation for Representation Learning | 1 INTRODUCTION . Mutual information ( MI ) is an appealing metric widely used in information theory and machine learning to quantify the amount of shared information between a pair of random variables . Specifically , given a pair of random variables x , y , the MI , denoted by I ( x ; y ) , is defined as I ( x ; y ) =... | This paper proposes MIGE---a novel estimator of the mutual information (MI) gradient, based on estimating the score function of an implicit distribution. To this end, the authors employ the spectral Stein gradient estimator (SSGE) and propose its scalable version based on random projections of the original input. The t... | SP:1e0fa3e10b19c54a0271b7cd2528ac8a3a51686a |
Mutual Information Gradient Estimation for Representation Learning | 1 INTRODUCTION . Mutual information ( MI ) is an appealing metric widely used in information theory and machine learning to quantify the amount of shared information between a pair of random variables . Specifically , given a pair of random variables x , y , the MI , denoted by I ( x ; y ) , is defined as I ( x ; y ) =... | This paper works out estimators for the gradient of Mutual Information (MI). The focus is on its recent popular use for representation learning. The insight the authors provide is to see encoding the representation as a ‘reparametrization’ of the data. This insight enables mathematical tools from the literature on ‘pat... | SP:1e0fa3e10b19c54a0271b7cd2528ac8a3a51686a |
Low Rank Training of Deep Neural Networks for Emerging Memory Technology | The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making “ at the edge. ” However , this work has traditionally focused on neural network inference , rather than training , due to memory and compute limitations , especially in emerging non-volatile ... | While inference on edge devices is a popular and well-studied problem in recent days, training on these devices comes with many challenges. This paper proposes a low-rank training schema that helps mitigate some of the critical challenges that occur during training models on NVM memory-based edge devices. Additionally,... | SP:f949cb0fd9e1b4afc31725a740ef87dd2d5d5a49 |
Low Rank Training of Deep Neural Networks for Emerging Memory Technology | The recent success of neural networks for solving difficult decision tasks has incentivized incorporating smart decision making “ at the edge. ” However , this work has traditionally focused on neural network inference , rather than training , due to memory and compute limitations , especially in emerging non-volatile ... | This paper proposes a low rank training method called the Streaming Kronecker Sum approximation (SKS algorithm) for training low precision models on edge devices. The authors compare their method to SGD for convolutional networks on MNIST and demonstrate improvements in terms of accuracy. The authors make use of the Op... | SP:f949cb0fd9e1b4afc31725a740ef87dd2d5d5a49 |
Hindsight Trust Region Policy Optimization | 1 INTRODUCTION . Reinforcement Learning has been a heuristic approach confronting a great many real-world problems from playing complex strategic games ( Mnih et al. , 2015 ; Silver et al. , 2016 ; Justesen et al. , 2019 ) to the precise control of robots ( Levine et al. , 2016 ; Mahler & Goldberg , 2017 ; Quillen et a... | This paper augments the TRPO policy optimization objective with hindsight data, where the hindsight data is generated from goals based on trajectories. The key contribution of the paper is based on deriving an on-policy adaptation of hindsight based TRPO, that can be useful for sparse reward environments. The paper dra... | SP:f150b1d2a3ad4d9614e1ef434ef18d742ee78e47 |
Hindsight Trust Region Policy Optimization | 1 INTRODUCTION . Reinforcement Learning has been a heuristic approach confronting a great many real-world problems from playing complex strategic games ( Mnih et al. , 2015 ; Silver et al. , 2016 ; Justesen et al. , 2019 ) to the precise control of robots ( Levine et al. , 2016 ; Mahler & Goldberg , 2017 ; Quillen et a... | The paper builds on top of prior work in hindsight policy gradients (Rauber et.al.) and trust region policy optimization (Schulman et.al.), proposing a hindsight trust region policy optimization. Conceptually this direction makes a lot of sense, since hindsight is in general shown to be useful when training goal condit... | SP:f150b1d2a3ad4d9614e1ef434ef18d742ee78e47 |
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities | Individuals with type 1 diabetes ( T1D ) lack the ability to produce the insulin their bodies need . As a result , they must continually make decisions about how much insulin to self-administer in order to adequately control their blood glucose levels . Longitudinal data streams captured from wearables , like continuou... | The paper describes an RL based approach to administer insulin for blood glucose control among type-1 diabetic patients. The paper formulates this blood glucose control problem as a closed-loop reinforcement learning problem and demonstrates its effectiveness on data generated from an FDA-approved simulator of glucor... | SP:33386a5a96124115a197a16ea1ca2f2ba326c34d |
Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities | Individuals with type 1 diabetes ( T1D ) lack the ability to produce the insulin their bodies need . As a result , they must continually make decisions about how much insulin to self-administer in order to adequately control their blood glucose levels . Longitudinal data streams captured from wearables , like continuou... | This paper examines reinforcement learning in the context of blood glucose control to help individuals with type 1 diabetes. The authors show that their methods lead to strong algorithms that can improve artificial pancreas systems. Their results are promising, and, very importantly, do not require meal announcements. ... | SP:33386a5a96124115a197a16ea1ca2f2ba326c34d |
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML | 1 INTRODUCTION . In numerous real-world applications , one is faced with various forms of adversary that are not accounted for by standard optimization algorithms . For instance , when training a machine learning model on user-provided data , malicious users can carry out a data poisoning attack : providing false data ... | The paper presents an algorithm for performing min-max optimisation without gradients and analyses its convergence. The algorithm is evaluated for the min-max problems that arise in the context of adversarial attacks. The presented algorithm is a natural application of a zeroth-order gradient estimator and the authors ... | SP:a85b6e1281b4c5f84e891b0897affe5971d4ff7a |
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML | 1 INTRODUCTION . In numerous real-world applications , one is faced with various forms of adversary that are not accounted for by standard optimization algorithms . For instance , when training a machine learning model on user-provided data , malicious users can carry out a data poisoning attack : providing false data ... | This paper considers zeroth-order method for min-max optimization (ZO-MIN-MAX) in two cases: one-sided black box (for outer minimization) and two-sided black box (for both inner maximization and outer minimization). Convergence analysis is carefully provided to show that ZO-MIN-MAX converges to a neighborhood of statio... | SP:a85b6e1281b4c5f84e891b0897affe5971d4ff7a |
OBJECT-ORIENTED REPRESENTATION OF 3D SCENES | 1 INTRODUCTION . The shortcomings of contemporary deep learning such as interpretability , sample efficiency , ability for reasoning and causal inference , transferability , and compositionality , are where the symbolic AI has traditionally shown its strengths ( Garnelo & Shanahan , 2019 ) . Thus , one of the grand cha... | The paper proposes a model building off of the generative query network model that takes in as input multiple images, builds a model of the 3D scene, and renders it. This can be trained end to end. The insight of the method is that one can factor the underlying representation into different objects. The system is train... | SP:41867edbd1bb96ff8340c8decefba2127a67dced |
OBJECT-ORIENTED REPRESENTATION OF 3D SCENES | 1 INTRODUCTION . The shortcomings of contemporary deep learning such as interpretability , sample efficiency , ability for reasoning and causal inference , transferability , and compositionality , are where the symbolic AI has traditionally shown its strengths ( Garnelo & Shanahan , 2019 ) . Thus , one of the grand cha... | The paper presents a framework for 3D representation learning from images of 2D scenes. The proposed architecture, which the authors call ROOTS (Representation of Object-Oriented Three-dimension Scenes), is based on the CGQN (Consistent Generative Query Networks) network. The paper provides 2 modifications. The represe... | SP:41867edbd1bb96ff8340c8decefba2127a67dced |
Higher-Order Function Networks for Learning Composable 3D Object Representations | We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second ‘ mapping ’ network . This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple ... | This paper presents a method for single image 3D reconstruction. It is inspired by implicit shape models, like presented in Park et al. and Mescheder et al., that given a latent code project 3D positions to signed distance, or occupancy values, respectively. However, instead of a latent vector, the proposed method dire... | SP:05a329e1e9faa9917c278dd2ba1eb5090189bdf9 |
Higher-Order Function Networks for Learning Composable 3D Object Representations | We present a new approach to 3D object representation where a neural network encodes the geometry of an object directly into the weights and biases of a second ‘ mapping ’ network . This mapping network can be used to reconstruct an object by applying its encoded transformation to points randomly sampled from a simple ... | This work is focused on learning 3D object representations (decoders) that can be computed more efficiently than existing methods. The computational inefficiency of these methods is that you learn a (big) fixed decoder for all objects (all z latents), and then need to apply it individually on either each point cloud p... | SP:05a329e1e9faa9917c278dd2ba1eb5090189bdf9 |
Rethinking Curriculum Learning With Incremental Labels And Adaptive Compensation | 1 INTRODUCTION . Deep networks have seen rich applications in high-dimensional problems characterized by a large number of labels and a high volume of samples . However , successfully training deep networks to solve problems under such conditions is mystifyingly hard ( Erhan et al . ( 2009 ) ; Larochelle et al . ( 2007... | This paper proposes a novel direction for curriculum learning. Previous works in the area of curriculum learning focused on choosing easier samples first and harder samples later when learning the neural network models. This is problematic since we need to first compute how difficult each samples are, which introduce... | SP:7f6ef5f3fa7627e799377aa06561904b80c5c1c4 |
Rethinking Curriculum Learning With Incremental Labels And Adaptive Compensation | 1 INTRODUCTION . Deep networks have seen rich applications in high-dimensional problems characterized by a large number of labels and a high volume of samples . However , successfully training deep networks to solve problems under such conditions is mystifyingly hard ( Erhan et al . ( 2009 ) ; Larochelle et al . ( 2007... | This paper makes the observation that a curriculum need not depend on the difficulty of examples, as most (maybe all) prior works do. They suggest instead a curriculum based on learning one class at a time, starting with one and masking the label of all others as 'unknown' (i.e. treating them as negative examples), and... | SP:7f6ef5f3fa7627e799377aa06561904b80c5c1c4 |
Support-guided Adversarial Imitation Learning | 1 INTRODUCTION . The class of Adversarial Imitation Learning ( AIL ) algorithms learns robust policies that imitate an expert ’ s actions from a small number of expert trajectories , without further access to the expert or environment signals . AIL iterates between refining a reward via adversarial training , and reinf... | The paper proposes an imitation learning algorithm that combines support estimation with adversarial training. The key idea is simple: multiply the reward from Random Expert Distillation (RED) with the reward from Generative Adversarial Imitation Learning (GAIL). The new reward combines the best of both methods. Like t... | SP:c3a5a5600463b8f590e9a2b10f7984973410b043 |
Support-guided Adversarial Imitation Learning | 1 INTRODUCTION . The class of Adversarial Imitation Learning ( AIL ) algorithms learns robust policies that imitate an expert ’ s actions from a small number of expert trajectories , without further access to the expert or environment signals . AIL iterates between refining a reward via adversarial training , and reinf... | This paper proposes an approach for improving adversarial imitation learning, by combining it with support-estimation-based imitation learning. In particular, the paper explores a combination of GAIL (Ho and Ermon, 2016) and RED (Wang et. al., 2019), where the reward for the policy-gradient is a product of the rewards ... | SP:c3a5a5600463b8f590e9a2b10f7984973410b043 |
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling | Imitation learning , followed by reinforcement learning algorithms , is a promising paradigm to solve complex control tasks sample-efficiently . However , learning from demonstrations often suffers from the covariate shift problem , which results in cascading errors of the learned policy . We introduce a notion of cons... | This paper tackles an issue imitation learning approaches face. More specifically, policies learned in this manner can often fail when they encounter new states not seen in demonstrations. The paper proposes a method for learning value functions that are more conservative on unseen states, which encourages the learned ... | SP:812c4e2bd2b3e6b25fc6869775bea958498cbfd1 |
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling | Imitation learning , followed by reinforcement learning algorithms , is a promising paradigm to solve complex control tasks sample-efficiently . However , learning from demonstrations often suffers from the covariate shift problem , which results in cascading errors of the learned policy . We introduce a notion of cons... | This work presents the value iteration with negative sampling (VINS) algorithm, a method for accelerating reinforcement learning using expert demonstrations. In addition to learning an expert policy through behavioral cloning, VINS learns an initial value function which is biased to assign smaller expected values to s... | SP:812c4e2bd2b3e6b25fc6869775bea958498cbfd1 |
Gradient-Based Neural DAG Learning | 1 INTRODUCTION . Structure learning and causal inference have many important applications in different areas of science such as genetics ( Koller & Friedman , 2009 ; Peters et al. , 2017 ) , biology ( Sachs et al. , 2005 ) and economics ( Pearl , 2009 ) . Bayesian networks ( BN ) , which encode conditional independenci... | The authors propose a prediction model for directed acyclic graphs (DAGs) over a fixed set of vertices based on a neural network. The present work follows the previous work on undirected acyclic graphs, where the key constraint is (3), ensuring the acyclic property. The proposed method performed favorably on artificial... | SP:c2dfaba3df490671f8ce20bf69df96d0887aa19d |
Gradient-Based Neural DAG Learning | 1 INTRODUCTION . Structure learning and causal inference have many important applications in different areas of science such as genetics ( Koller & Friedman , 2009 ; Peters et al. , 2017 ) , biology ( Sachs et al. , 2005 ) and economics ( Pearl , 2009 ) . Bayesian networks ( BN ) , which encode conditional independenci... | This work addresses the problem of learning the structure of directed acyclic graphs in the presence of nonlinearities. The proposed approach is an extension of the NOTEARS algorithm which uses a neural network for each node in the graph during structure learning. This adaptation allows for non-linear relationships to ... | SP:c2dfaba3df490671f8ce20bf69df96d0887aa19d |
LAMOL: LAnguage MOdeling for Lifelong Language Learning | 1 INTRODUCTION . The current dominant paradigm for machine learning is to run an algorithm on a given dataset to produce a trained model specifically for a particular purpose ; this is isolated learning ( Chen & Liu , 2016 , p. 150 ) . In isolated learning , the model is unable to retain and accumulate the knowledge it... | The paper presents a new NN architecture designed for life-long learning of natural language processing. As well depicted in Figure 2, the proposed network is trained to generate the correct answers and training samples at the same time. This prevents the "catastrophic forgetting" of an old task. Compared to the old me... | SP:4aebddd56e10489765e302e291cf41589d02b530 |
LAMOL: LAnguage MOdeling for Lifelong Language Learning | 1 INTRODUCTION . The current dominant paradigm for machine learning is to run an algorithm on a given dataset to produce a trained model specifically for a particular purpose ; this is isolated learning ( Chen & Liu , 2016 , p. 150 ) . In isolated learning , the model is unable to retain and accumulate the knowledge it... | This paper studies the problem of lifelong language learning. The core idea underlying the algorithm includes two parts: 1. Consider the NLP tasks as QA and then train a LM model that generates an answer based on the context and the question; 2. to generate samples representing previous tasks before training on a new t... | SP:4aebddd56e10489765e302e291cf41589d02b530 |
Learning Underlying Physical Properties From Observations For Trajectory Prediction | 1 INTRODUCTION . Games that follow Newton ’ s laws of physics despite being a relatively easy task for humans , remain to be a challenging task for artificially intelligent agents due to the requirements for an agent to understand underlying physical laws and relationships between available player ’ s actions and their... | The problem addressed by this paper is the estimation of trajectories of moving objects thrown / launched by a user, in particular in computer games like angry birds or basketball simulation games. A deep neural network is trained on a small dataset of ~ 300 trajectories and estimates the underlying physical properties... | SP:bce4d9d2825454f2b345f4650abac10efee7c2fb |
Learning Underlying Physical Properties From Observations For Trajectory Prediction | 1 INTRODUCTION . Games that follow Newton ’ s laws of physics despite being a relatively easy task for humans , remain to be a challenging task for artificially intelligent agents due to the requirements for an agent to understand underlying physical laws and relationships between available player ’ s actions and their... | This paper proposes an architecture that encodes a known physics motion equation of a trajectory of a moving object. The modeled equation has 3 variables and the network works in a latent space- contrary to taking raw images. It uses an auxiliary network (named InferNet) to train the final one used at inference time (n... | SP:bce4d9d2825454f2b345f4650abac10efee7c2fb |
High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection | 1 INTRODUCTION . Class-specific object boundary extraction from images is a fundamental problem in Computer Vision ( CV ) . It has been used as a basic module for several applications including object localization [ Yu et al . ( 2018a ) ; Wang et al . ( 2015 ) ] , 3D reconstruction [ Lee et al . ( 2009 ) ; Malik & Mayd... | The suggest two improvements to boundary detection models: (1) a curriculum learning approach, and (2) augmenting CNNs with features derived from a wavelet transform. For (1), they train half of the epochs with a target boundary that is the intersection between a Canny edge filter and the dilated groundtruth. The secon... | SP:f6af733aa873bf6ee0f69ec868a2d7a493a0dd0b |
High-Frequency guided Curriculum Learning for Class-specific Object Boundary Detection | 1 INTRODUCTION . Class-specific object boundary extraction from images is a fundamental problem in Computer Vision ( CV ) . It has been used as a basic module for several applications including object localization [ Yu et al . ( 2018a ) ; Wang et al . ( 2015 ) ] , 3D reconstruction [ Lee et al . ( 2009 ) ; Malik & Mayd... | The main idea of the paper is adding a curriculum learning-based extension to CASEnet, a boundary detection method from 2017. In the first phase, the loss emphasizes easier examples with high gradient in the image, and in the second phase, the method is trained on all boundary pixels. This change seems to improve edge ... | SP:f6af733aa873bf6ee0f69ec868a2d7a493a0dd0b |
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees | 1 INTRODUCTION . Deep Reinforcement Learning ( DRL ) has achieved many recent breakthroughs in challenging domains such as Go ( Silver et al. , 2016 ) . While using neural networks for encoding state representations allow DRL agents to learn policies for tasks with large state spaces , the learned policies are not inte... | The paper proposes an extension to the Viper[1] method for interpreting and verifying deep RL policies by learning a mixture of decision trees to mimic the originally learned policy. The proposed approach can imitate the deep policy better compared with Viper while preserving verifiability. Empirically the proposed met... | SP:91fbd1f4774de6619bd92d37e1a1b1e7f2ed96f3 |
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees | 1 INTRODUCTION . Deep Reinforcement Learning ( DRL ) has achieved many recent breakthroughs in challenging domains such as Go ( Silver et al. , 2016 ) . While using neural networks for encoding state representations allow DRL agents to learn policies for tasks with large state spaces , the learned policies are not inte... | The paper proposes a method (MOET) to distillate a reinforcement learning policy represented by a deep neural network into an ensemble of decision trees. The main objective of this procedure is to obtain an "interpretable" and verifiable policy while maintaining the performance of the policy. The authors build over th... | SP:91fbd1f4774de6619bd92d37e1a1b1e7f2ed96f3 |
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors | 1 INTRODUCTION . Spatial navigation is an important task that requires a correct internal representation of the world , and thus its mechanistic underpinnings have attracted the attention of scientists for a long time ( O ’ Keefe & Nadel , 1978 ) . A standard tool for navigation is a euclidean map , and this naturally ... | This paper studies the internal representations of recurrent neural networks trained on navigation tasks. By varying the weight of different terms in an objective used for supervised pre-training, RNNs are created that either use path integration or landmark memory for navigation. The paper shows that the pretraining m... | SP:ddc70109c59cf0db7fe020300ab762a5ac57bd93 |
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors | 1 INTRODUCTION . Spatial navigation is an important task that requires a correct internal representation of the world , and thus its mechanistic underpinnings have attracted the attention of scientists for a long time ( O ’ Keefe & Nadel , 1978 ) . A standard tool for navigation is a euclidean map , and this naturally ... | This paper explores how pre-training a recurrent network on different navigational objectives confers different benefits when it comes to solving downstream tasks. First, networks are pretrained on an objective that either emphasizes position (path integration) or landmark memory (identity of the last wall encountered)... | SP:ddc70109c59cf0db7fe020300ab762a5ac57bd93 |
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