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Disagreement-Regularized Imitation Learning | 1 INTRODUCTION . Training artificial agents to perform complex tasks is essential for many applications in robotics , video games and dialogue . If success on the task can be accurately described using a reward or cost function , reinforcement learning ( RL ) methods offer an approach to learning policies which has pro... | The paper aims to address the covariate shift issue of behavior cloning (BC). The main idea of the paper is to learn a policy by minimizing a BC loss and an uncertainty loss. This uncertainty loss is defined as a variance of a policy posterior given by demonstration. To approximate this posterior, the paper uses an ens... | SP:5f026e00085a3f771abf068bd884e27a6f9d9e44 |
Disagreement-Regularized Imitation Learning | 1 INTRODUCTION . Training artificial agents to perform complex tasks is essential for many applications in robotics , video games and dialogue . If success on the task can be accurately described using a reward or cost function , reinforcement learning ( RL ) methods offer an approach to learning policies which has pro... | The paper proposes an imitation learning algorithm that combines behavioral cloning with a regularizer that encourages the agent to visit states similar to the demonstrated states. The key idea is to use ensemble disagreement to approximate uncertainty, and use RL to train the imitation agent to visit states in which a... | SP:5f026e00085a3f771abf068bd884e27a6f9d9e44 |
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY | Differently from the popular Deep Q-Network ( DQN ) learning , Alternating Qlearning ( AltQ ) does not fully fit a target Q-function at each iteration , and is generally known to be unstable and inefficient . Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to impro... | This paper is well-written and it provides a convergence result for traditional Q-learning, with linear function approximation, when using an Adam-like update (AMSGrad). It does the same for a variation of this algorithm where the momentum-like term is reset every now and then. This second result is not that exciting a... | SP:2bf8148e5dadace0b6dd4b9f715fa8261f2a52db |
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY | Differently from the popular Deep Q-Network ( DQN ) learning , Alternating Qlearning ( AltQ ) does not fully fit a target Q-function at each iteration , and is generally known to be unstable and inefficient . Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to impro... | This paper claims to propose a method to train q-based agents that use “alternating” Q-learning. However, the alternating approach given in the paper appears to be the normal Bellman update implemented in most versions of DQN. Furthermore, the citation given for AltQ (Mnih et al. 2016) makes no mention of the term “Alt... | SP:2bf8148e5dadace0b6dd4b9f715fa8261f2a52db |
Growing Action Spaces | In complex tasks , such as those with large combinatorial action spaces , random exploration may be too inefficient to achieve meaningful learning progress . In this work , we use a curriculum of progressively growing action spaces to accelerate learning . We assume the environment is out of our control , but that the ... | The paper presents a method of scaling up towards action spaces, that exhibit natural hierarchies (such as a controllable resolution of actions), throughout joint training of Q-functions over these. Authors notice, and exploit a few interesting properties, such as inequalities that emerge when action spaces form strict... | SP:2036673d54d07683d1dfdad4567ea18029344359 |
Growing Action Spaces | In complex tasks , such as those with large combinatorial action spaces , random exploration may be too inefficient to achieve meaningful learning progress . In this work , we use a curriculum of progressively growing action spaces to accelerate learning . We assume the environment is out of our control , but that the ... | This paper proposes a method to progressively explore the action space for RL. The proposed method is called “growing action spaces”. The basic idea is that actions can usually be grouped by a hierarchical structure: the lowest level is the coarsest and higher levels gradually refine the action partition. This method e... | SP:2036673d54d07683d1dfdad4567ea18029344359 |
On the Invertibility of Invertible Neural Networks | 1 INTRODUCTION . Invertible neural networks ( INNs ) have become a standard building block in the deep learning toolkit . Invertibility is useful for training generative models with exact likelihoods ( Dinh et al. , 2014 ; 2017 ; Kingma & Dhariwal , 2018 ; Kingma et al. , 2016 ; Behrmann et al. , 2019 ; Chen et al. , 2... | The paper claims that for invertible neural networks, mathematical guarantees on invertibility is not enough, and we also require numerical invertibility. To this end, the lipschitz constants/condition numbers of Jacobians of both the forward and inverse maps of invertible NNs based on coupling layers are examined math... | SP:76b0a90c46bc2151088210ca47ea4761706f1716 |
On the Invertibility of Invertible Neural Networks | 1 INTRODUCTION . Invertible neural networks ( INNs ) have become a standard building block in the deep learning toolkit . Invertibility is useful for training generative models with exact likelihoods ( Dinh et al. , 2014 ; 2017 ; Kingma & Dhariwal , 2018 ; Kingma et al. , 2016 ; Behrmann et al. , 2019 ; Chen et al. , 2... | This paper analyses the numerical invertibility of analytically invertible neural networks (INN). The numerical invertibility depends on the Lipschitz constant of the respective transformation. The paper provides Lipschitz bounds on the components of building blocks for certain INN architectures, which would guarantee ... | SP:76b0a90c46bc2151088210ca47ea4761706f1716 |
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer | 1 INTRODUCTION . Transferring solution strategies from one problem to another is a crucial ability for intelligent behavior ( Silver et al. , 2013 ) . Current learning systems can learn a multitude of specialized tasks , but extracting the underlying structure of the solution for effective transfer is an open research ... | This paper introduces a neural controller architecture for learning abstract algorithmic solutions to search and planning problems. By combining abstract and domain-specific components, the model is able to mimic two classical algorithms quite closely across several domains. The precision of the learning is very high; ... | SP:a1d1e8d13b1df53435caa45e5fed856fcdd1b6ec |
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer | 1 INTRODUCTION . Transferring solution strategies from one problem to another is a crucial ability for intelligent behavior ( Silver et al. , 2013 ) . Current learning systems can learn a multitude of specialized tasks , but extracting the underlying structure of the solution for effective transfer is an open research ... | This paper proposes modifications and modular extensions to the differential neural computer (DNC). The approach is nicely modular, decoupling the data modules from algorithmic modules. This enables the authors to pretrain the data modules with supervised learning and to train the small algorithmic modules with neural ... | SP:a1d1e8d13b1df53435caa45e5fed856fcdd1b6ec |
Few-Shot Regression via Learning Sparsifying Basis Functions | Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples . Previous few-shot learning works have mainly focused on classification and reinforcement learning . In this paper , we propose a method that focuses on regression tasks . Our model is based on th... | The paper proposes a regression approach that, given a few training (support) samples of a regression task (input and desired output pairs), should be able to output the values of the target function on additional (query) inputs. The proposed method is to learn a set of basis functions (MLPs) and a weight generator tha... | SP:da33f43dc72578ff039a1843c3bbbfc70ed4a685 |
Few-Shot Regression via Learning Sparsifying Basis Functions | Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples . Previous few-shot learning works have mainly focused on classification and reinforcement learning . In this paper , we propose a method that focuses on regression tasks . Our model is based on th... | The authors propose using sparse adaptive basis function models for few shot regression. The basis functions and the corresponding weights are generated via respective networks whose parameters are shared across all tasks. Elastic net regularization is used to encourage task specific sparsity in the weights, the idea ... | SP:da33f43dc72578ff039a1843c3bbbfc70ed4a685 |
Clustered Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) ( Sutton & Barto , 1998 ) studies how an agent can maximize its cumulative reward in an unknown environment , by learning through exploration and exploitation . A key challenge in RL is to balance the relationship between exploration and exploitation . If the agent explore... | This paper presents a clear approach to improve the exploration strategy in reinforcement learning, which is named clustered reinforcement learning. The approach tries to push the agent to explore more states with high novelty and quality. It is done by adding a bonus reward shown in Eq. (3) to the reward function. Th... | SP:e1726e0e4f65ec99676002eca4ad9cdf27f60b56 |
Clustered Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) ( Sutton & Barto , 1998 ) studies how an agent can maximize its cumulative reward in an unknown environment , by learning through exploration and exploitation . A key challenge in RL is to balance the relationship between exploration and exploitation . If the agent explore... | This paper proposed a clustering based algorithm to improve the exploration performance in reinforcement learning. Similar to the count based approaches, the novelty of a new state was computed based on the statistics of the corresponding clusters. This exploration bonus was then combined with the TRPO algorithm to obt... | SP:e1726e0e4f65ec99676002eca4ad9cdf27f60b56 |
Non-Autoregressive Dialog State Tracking | 1 INTRODUCTION . In task-oriented dialogues , a dialogue agent is required to assist humans for one or many tasks such as finding a restaurant and booking a hotel . As a sample dialogue shown in Table 1 , each user utterance typically contains important information identified as slots related to a dialogue domain such ... | This paper proposed a model that is capable of tracking dialogue states in a non-recursive fashion. The main techniques behind the non-recursive model is similar to that of the ICLR 2018 paper "NON-AUTOREGRESSIVE NEURAL MACHINE TRANSLATION". Unfortunately, as state tacking can be formulated as one special case of seque... | SP:333b75014a3cde3eb10486b9b7db6eea42db3196 |
Non-Autoregressive Dialog State Tracking | 1 INTRODUCTION . In task-oriented dialogues , a dialogue agent is required to assist humans for one or many tasks such as finding a restaurant and booking a hotel . As a sample dialogue shown in Table 1 , each user utterance typically contains important information identified as slots related to a dialogue domain such ... | The authors build on recent work for non-autoregressive encoder-decoder models in the context of machine translation (most significantly [Gu, et al., ICLR18]) and adapt this to dialogue state tracking. Specifically, as in [Gu, et al, ICLR18], they use a fertility decoder modified for DST to be on a per-slot basis which... | SP:333b75014a3cde3eb10486b9b7db6eea42db3196 |
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference | Github : http : //github.com/google-research/seed_rl . 1 INTRODUCTION . The field of reinforcement learning ( RL ) has recently seen impressive results across a variety of tasks . This has in part been fueled by the introduction of deep learning in RL and the introduction of accelerators such as GPUs . In the very rece... | The paper presents SEED RL, which is a scalable reinforcement learning agent. The approach restructure the interface / division of functionality between the actors (environments) and the learner as compared to the distributed approach in IMPALA (a state-of-the-art distributed RL framework). Most importantly, the model ... | SP:7f1af600e64c0ad693a9b1cc198bbaf39cd884c6 |
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference | Github : http : //github.com/google-research/seed_rl . 1 INTRODUCTION . The field of reinforcement learning ( RL ) has recently seen impressive results across a variety of tasks . This has in part been fueled by the introduction of deep learning in RL and the introduction of accelerators such as GPUs . In the very rece... | This paper presents a scalable reinforcement learning training architecture which combines a number of modern engineering advances to address the inefficiencies of prior methods. The proposed architecture shows good performance on a wide variety of benchmarks from ALE to DeepMind Lab and Google Research Football. Impor... | SP:7f1af600e64c0ad693a9b1cc198bbaf39cd884c6 |
ROBUST GENERATIVE ADVERSARIAL NETWORK | INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have been enjoying much attention recently due to their great success on different tasks and datasets ( Radford et al. , 2015 ) ( Salimans et al. , 2016 ) ( Ho & Ermon , 2016 ) ( Li et al. , 2017 ) ( Chongxuan et al. , 2017 ) . The fra... | Developing stable GAN training method has gained much attention these years. This paper propose to tackle this issue via involving distributionally robust optimization into GAN training. Its main contribution is to combine Sinha et al with GAN, proposing a new GAN training method on the basis of vanilla GAN. Relative ... | SP:1e09b69a3d713355bd967d8205fde97a911042e7 |
ROBUST GENERATIVE ADVERSARIAL NETWORK | INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have been enjoying much attention recently due to their great success on different tasks and datasets ( Radford et al. , 2015 ) ( Salimans et al. , 2016 ) ( Ho & Ermon , 2016 ) ( Li et al. , 2017 ) ( Chongxuan et al. , 2017 ) . The fra... | The present work proposes to combine GANs with adversarial training replacing the original GAN lass with a mixture of the original GAN loss and an adversarial loss that applies an adversarial perturbation to both the input image of the discriminator, and to the input noise of the generator. The resulting algorithm is c... | SP:1e09b69a3d713355bd967d8205fde97a911042e7 |
Deep Symbolic Superoptimization Without Human Knowledge | 1 INTRODUCTION . Superoptimization refers to the task of simplifying and optimizing over a set of machine instructions , or code ( Massalin , 1987 ; Schkufza et al. , 2013 ) , which is a fundamental problem in computer science . As an important direction in superoptimization , symbolic expression simplification , or sy... | This paper presents a method for symbolic superoptimization — the task of simplifying equations into equivalent expressions. The main goal is to design a method that does not rely on human input in defining equivalence classes, which should improve scalability of the simplification method to a larger set of expressions... | SP:c163bfc3f289c0c63ec25bbd21a63a921518ed22 |
Deep Symbolic Superoptimization Without Human Knowledge | 1 INTRODUCTION . Superoptimization refers to the task of simplifying and optimizing over a set of machine instructions , or code ( Massalin , 1987 ; Schkufza et al. , 2013 ) , which is a fundamental problem in computer science . As an important direction in superoptimization , symbolic expression simplification , or sy... | This paper provides a novel approach to the problem of simplifying symbolic expressions without relying on human input and information. To achieve this, they apply a REINFORCE framework with a reward function involving the number of symbols in the final output together with a probabilistic testing scheme to determine e... | SP:c163bfc3f289c0c63ec25bbd21a63a921518ed22 |
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints | In most practical settings and theoretical analyses , one assumes that a model can be trained until convergence . However , the growing complexity of machine learning datasets and models may violate such assumptions . Indeed , current approaches for hyper-parameter tuning and neural architecture search tend to be limit... | This work presents a simple technique for tuning the learning rate for Neural Network training when under a "budget" -- the budget here is specified as a fixed number of epochs that is expected to be a small fraction of the total number of epochs required to achieve maximum accuracy. The main contribution of this paper... | SP:2fa2a5ffa0193c0e5840bd18dc500739d2c369e0 |
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints | In most practical settings and theoretical analyses , one assumes that a model can be trained until convergence . However , the growing complexity of machine learning datasets and models may violate such assumptions . Indeed , current approaches for hyper-parameter tuning and neural architecture search tend to be limit... | This paper analyzed which learning rate schedule (LRS) should be used when the budget (number of iteration) is limited. First, the authors have introduced the concept of BAS (Budget-Aware Schedule). Various LRSs are classified, and it is experimentally shown that the LRSs based on BAS performed better. Among them, the ... | SP:2fa2a5ffa0193c0e5840bd18dc500739d2c369e0 |
Translation Between Waves, wave2wave | 1 INTRODUCTION . The problem of shortage of training data but can be supplied by other sensor data is called an opportunistic sensor problem ( Roggen et al. , 2013 ) . For example in human activity logs , the video data can be missing in bathrooms by ethical reasons but can be supplied by environmental sensors which ha... | In this paper, the authors propose modifications to baseline seq-to-seq systems for wave-to-wave translation. To handle possibly long inputs and outputs, as well as significant length differences, they propose to use sliding windows. For high-dimensional outputs, they use an iterative approach predicting each dimension... | SP:4fdd94362be6718ab249cdb8da4e75b9eade64bd |
Translation Between Waves, wave2wave | 1 INTRODUCTION . The problem of shortage of training data but can be supplied by other sensor data is called an opportunistic sensor problem ( Roggen et al. , 2013 ) . For example in human activity logs , the video data can be missing in bathrooms by ethical reasons but can be supplied by environmental sensors which ha... | This work explains how to use a seq2seq encoder-decoder neural network on the case of multivariate time series. The authors name this particular application of seq2seq the wave2wave network. Given a multivariate time series covering a time interval, it is split into subintervals of equal length, such that each block is... | SP:4fdd94362be6718ab249cdb8da4e75b9eade64bd |
Policy Optimization with Stochastic Mirror Descent | 1 INTRODUCTION . Reinforcement learning ( RL ) is one of the most wonderful fields of artificial intelligence , and it has achieved great progress recently ( Mnih et al. , 2015 ; Silver et al. , 2017 ) . To learn the optimal policy from the delayed reward decision system is the fundamental goal of RL . Policy gradient ... | This paper proposed a variant of policy gradient algorithm with mirror descent update, which is a natural generalization of projected policy gradient descent. The authors also proposed a variance reduced policy gradient algorithm following the variance reduction techniques in optimization. The authors further proved th... | SP:13d8b88675da21a709b023c18bf47d6fc2c12924 |
Policy Optimization with Stochastic Mirror Descent | 1 INTRODUCTION . Reinforcement learning ( RL ) is one of the most wonderful fields of artificial intelligence , and it has achieved great progress recently ( Mnih et al. , 2015 ; Silver et al. , 2017 ) . To learn the optimal policy from the delayed reward decision system is the fundamental goal of RL . Policy gradient ... | This paper proposes MPO, a policy optimization method with convergence guarantees based on stochastic mirror descent that uses the average of previous gradients to update the policy parameters. A lower-variance method, VRMPO, is then proposed that matches the best known convergence rate in the in the literature. Experi... | SP:13d8b88675da21a709b023c18bf47d6fc2c12924 |
Variance Reduction With Sparse Gradients | 1 INTRODUCTION . Optimization tools for machine learning applications seek to minimize the finite sum objective min x∈Rd f ( x ) , 1 n n∑ i=1 fi ( x ) , ( 1 ) where x is a vector of parameters , and fi : Rd → R is the loss associated with sample i. Batch SGD serves as the prototype for modern stochastic gradient method... | This paper aims at improving the computational cost of variance reduction methods while preserving their benefits regarding the fast provable convergence. The existing variance reduction based methods suffer from higher per-iteration gradient query complexity as compared to the vanilla mini-batch SGD, which limits thei... | SP:24fb2650085abd5599f3dcd187a62a514608423a |
Variance Reduction With Sparse Gradients | 1 INTRODUCTION . Optimization tools for machine learning applications seek to minimize the finite sum objective min x∈Rd f ( x ) , 1 n n∑ i=1 fi ( x ) , ( 1 ) where x is a vector of parameters , and fi : Rd → R is the loss associated with sample i. Batch SGD serves as the prototype for modern stochastic gradient method... | The author(s) provide a method which combines some property of SCGS method and SpiderBoost. Theoretical results are provided and achieve the state-of-the-art complexity, which match the one of SpiderBoost. Numerical experiments show some advantage compared to SpiderBoost on some deep neural network architecture for som... | SP:24fb2650085abd5599f3dcd187a62a514608423a |
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph | 1 INTRODUCTION . Representation learning on dynamically-evolving , graph-structured data has emerged as an important problem in a wide range of applications , including social network analysis ( Zhou et al. , 2018a ; Trivedi et al. , 2019 ) , knowledge graph reasoning ( Trivedi et al. , 2017 ; Nguyen et al. , 2018 ; Ka... | This paper properly applied several technique from RNN and graph neural networks to model dynamically-evolving, multi-relational graph data. There are two key component: a RNN to encode temporal information from the past event sequences, and a neighborhood aggregator collects the information from the neighbor nodes. Th... | SP:d94b0a398257e68c8888f0fdb9e6765881f798af |
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph | 1 INTRODUCTION . Representation learning on dynamically-evolving , graph-structured data has emerged as an important problem in a wide range of applications , including social network analysis ( Zhou et al. , 2018a ; Trivedi et al. , 2019 ) , knowledge graph reasoning ( Trivedi et al. , 2017 ; Nguyen et al. , 2018 ; Ka... | The paper proposes a recurrent and autorgressive architecture to model temporal knowledge graphs and perform multi-time-step inference in the form of future link prediction. Specifically, given a historical sequence of graphs at discrete time points, the authors build sequential probabilistic approach to infer the next... | SP:d94b0a398257e68c8888f0fdb9e6765881f798af |
Identifying Weights and Architectures of Unknown ReLU Networks | 1 INTRODUCTION . The behavior of deep neural networks is as complex as it is powerful . The relation of individual parameters to the network ’ s output is highly nonlinear and is generally unclear to an external observer . Consequently , it has been widely supposed in the field that it is impossible to recover the para... | This paper introduces an approach to recover weights of ReLU neural networks by querying the network with specifically constructed inputs. The authors notice that the decision regions of such networks are piece-wise linear corresponding to activations of individual neurons. This allows to identify hyperplanes that cons... | SP:6ecf7180d11e9eaf100d489c1c20123cde7a258d |
Identifying Weights and Architectures of Unknown ReLU Networks | 1 INTRODUCTION . The behavior of deep neural networks is as complex as it is powerful . The relation of individual parameters to the network ’ s output is highly nonlinear and is generally unclear to an external observer . Consequently , it has been widely supposed in the field that it is impossible to recover the para... | This paper introduces a procedure for reconstructing the architecture and weights of deep ReLU network, given only the ability to query the network (observe network outputs for a sequence of inputs). The algorithm takes advantage of the piecewise linearity of ReLU networks and an analysis by [Hanin and Rolnick, 2019b]... | SP:6ecf7180d11e9eaf100d489c1c20123cde7a258d |
ISBNet: Instance-aware Selective Branching Networks | Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search ( NAS ) . Although remarkable efficiency and accuracy have been achieved , existing expert designed and NAS models neglect the fact that input instances are of varying complexity and thus different amount... | Neural architecture search usually aims to find a single fixed architecture for the task of interest. The paper proposes to condition the architecture on the input instances by introducing a "selection network" that learns to retain a subset of branches in the architecture during each inference pass. The intuition is t... | SP:cd75cf49f7e773f69c08c6489ec9f63f9a2de4ad |
ISBNet: Instance-aware Selective Branching Networks | Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search ( NAS ) . Although remarkable efficiency and accuracy have been achieved , existing expert designed and NAS models neglect the fact that input instances are of varying complexity and thus different amount... | This paper proposes an instance-aware dynamic network, ISBNet, for efficient image classification. The network consists of layers of cell structures with multiple branches within. During the inference, the network uses SelectionNet to compute a "calibration weight matrix", which essentially controls which branches with... | SP:cd75cf49f7e773f69c08c6489ec9f63f9a2de4ad |
SPECTRA: Sparse Entity-centric Transitions | Learning an agent that interacts with objects is ubiquituous in many RL tasks . In most of them the agent ’ s actions have sparse effects : only a small subset of objects in the visual scene will be affected by the action taken . We introduce SPECTRA , a model for learning slot-structured transitions from raw visual ob... | This paper introduces a model that learns a slot-based representation, along with a transition model to predict the evolution of these representations in a sparse fashion, all in a fully unsupervised way. This is done by leveraging a self-attention mechanism to decide which slots should be updated in a given transition... | SP:b2a151ab2ee385b50881be2865f6503902f2fcc9 |
SPECTRA: Sparse Entity-centric Transitions | Learning an agent that interacts with objects is ubiquituous in many RL tasks . In most of them the agent ’ s actions have sparse effects : only a small subset of objects in the visual scene will be affected by the action taken . We introduce SPECTRA , a model for learning slot-structured transitions from raw visual ob... | This paper proposes to use a ‘slot-based’ (factored) representation of a ‘scene’ s.t. a forward model learned over some observed transitions only requires sparse updates to the current representation. The results show that jointly learning the forward model and the scene representation encourages meaningful ‘entities’ ... | SP:b2a151ab2ee385b50881be2865f6503902f2fcc9 |
Model-based reinforcement learning for biological sequence design | The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact . Doing so presents a challenging black-box optimization problem characterized by the large-batch , low round setting due to the need for labor-intensive wet lab evaluations . In response , we propo... | This paper apply a model-based RL algorithm, DyNA-PPO for designing biological sequences. By being model-based, this algorithm is sample efficiency compared to model-free RL algorithms. This advantage is attractive and important in the context of biological sequence design since the designed is constrained to be done i... | SP:3d76cac4f6c4d3bb1003b739801a4981c0db00b8 |
Model-based reinforcement learning for biological sequence design | The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact . Doing so presents a challenging black-box optimization problem characterized by the large-batch , low round setting due to the need for labor-intensive wet lab evaluations . In response , we propo... | In this work the authors propose a framework for combinatorial optimisation problems in the conditions that the measurements are expensive. The basic idea is to make an approximation of the reward function and then train the policy using the simulated environment based on the approximated reward function. The applicati... | SP:3d76cac4f6c4d3bb1003b739801a4981c0db00b8 |
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination | 1 INTRODUCTION . Cooperative multiagent reinforcement learning ( MARL ) studies how multiple agents can learn to coordinate as a team toward maximizing a global objective . Cooperative MARL has been applied to many real world applications such as air traffic control ( Tumer and Agogino , 2007 ) , multi-robot coordinati... | This paper proposes an algorithm to learn coordination strategies for multi-agent reinforcement learning. It combines gradient-based optimization (Actor-critic) with Neuroevolution (genetic algorithms style). Specifically, Actor-critic is used to train an ensemble of agents (referred to as “team”) using a manually desi... | SP:b1f2e7dee0606c25926a81ac32462c8bd2cb4808 |
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination | 1 INTRODUCTION . Cooperative multiagent reinforcement learning ( MARL ) studies how multiple agents can learn to coordinate as a team toward maximizing a global objective . Cooperative MARL has been applied to many real world applications such as air traffic control ( Tumer and Agogino , 2007 ) , multi-robot coordinati... | This paper proposes to use a two-level optimization process to solve the challenge of optimizing the team reward and the agent's reward simultaneously, which are often not aligned. It applies the evolutionary algorithm to optimize the sparse team reward, while using RL (TD3) to optimize the agent's dense reward. In thi... | SP:b1f2e7dee0606c25926a81ac32462c8bd2cb4808 |
Never Give Up: Learning Directed Exploration Strategies | 1 INTRODUCTION . The problem of exploration remains one of the major challenges in deep reinforcement learning . In general , methods that guarantee finding an optimal policy require the number of visits to each state–action pair to approach infinity . Strategies that become greedy after a finite number of steps may ne... | The paper proposes a novel intrinsic reward/curiosity metric that combines both episodic and “life-long” novelty. Essentially two competing pressures that push agents to explore as many novel states in a single rollout as possible and to explore as many states as possible as evenly as possible. The primary contribution... | SP:ea8267af45b09cc35349456d85eb39c58447e319 |
Never Give Up: Learning Directed Exploration Strategies | 1 INTRODUCTION . The problem of exploration remains one of the major challenges in deep reinforcement learning . In general , methods that guarantee finding an optimal policy require the number of visits to each state–action pair to approach infinity . Strategies that become greedy after a finite number of steps may ne... | The work is motivated by the goal of having a comprehensive exploration of an agent in deep RL. For achieving that, the authors propose a count-based NGU agent, combining intrinsic and extrinsic bonuses as new rewards. An extrinsic/ long-term novelty module is used to control the amount of exploration across episodes, ... | SP:ea8267af45b09cc35349456d85eb39c58447e319 |
Hidden incentives for self-induced distributional shift | 1 INTRODUCTION . Consider a household robot , one of whose duties is to predict when its owner will ask it for coffee . We would like the robot to notice its owners preference for having coffee in the morning , but we would not want the robot to prevent its owner from sleeping late just because the robot is unsure if t... | The authors study the phenomena of self-introduced distributional shift. They define the term along with the term hidden incentives for distributional shift. The latter describes factors that motivate the learner to change the distribution in order to achieve a higher performance. The authors study both phenomena in tw... | SP:1ac8384ea71a1d51086464a466cd3167da4336c1 |
Hidden incentives for self-induced distributional shift | 1 INTRODUCTION . Consider a household robot , one of whose duties is to predict when its owner will ask it for coffee . We would like the robot to notice its owners preference for having coffee in the morning , but we would not want the robot to prevent its owner from sleeping late just because the robot is unsure if t... | The main idea of the paper: When using meta-learning there is an inherent incentive for the learner to win by making the task easier. The authors generalise this effect to a larger class of problems where the learning framework induces a set of Hidden Incentive for Distributional Shift (HIDS) and introduce Context Swap... | SP:1ac8384ea71a1d51086464a466cd3167da4336c1 |
Hierarchical Disentangle Network for Object Representation Learning | 1 INTRODUCTION . Representation learning , as one basic and hot topic in machine learning and computer vision community , has achieved significant progress in recent years on different tasks such as recognition ( Russakovsky et al. , 2015 ) , detection ( Ren et al. , 2015 ; Redmon et al. , 2016 ; Liu et al. , 2016b ) a... | This paper studies the problem of learning disentangled representation in a hierarchical manner. It proposed a hierarchical disentangle network (HDN) which tackles the disentangling process in a coarse-to-fine manner. Specifically, common representations are captured at root level and unique representations are learned... | SP:efc663895e7ee0d78501c66be7c242d7f882d45d |
Hierarchical Disentangle Network for Object Representation Learning | 1 INTRODUCTION . Representation learning , as one basic and hot topic in machine learning and computer vision community , has achieved significant progress in recent years on different tasks such as recognition ( Russakovsky et al. , 2015 ) , detection ( Ren et al. , 2015 ; Redmon et al. , 2016 ; Liu et al. , 2016b ) a... | This paper proposed the hierarchical disentangle network (HDN) that leverages hierarchical characteristics of object categories to learn disentangled representation in multiple levels. Their coarse-to-fine manner approach allows each level to focus on learning specific representations in its granularity. This is achiev... | SP:efc663895e7ee0d78501c66be7c242d7f882d45d |
Learning from Label Proportions with Consistency Regularization | 1 INTRODUCTION . In traditional supervised learning , a classifier is trained on a dataset where each instance is associated with a class label . However , label annotation can be expensive or difficult to obtain for some applications . Take the embryo selection as an example ( Hernández-González et al. , 2018 ) . To... | of the paper: Learning from label proportions (LLP) is an area in machine learning that tries to learn a classifier that predicts labels of instances, with only bag-level aggregated labels given at the training stage. Instead of proposing a loss specialized for this problem, this paper proposes a regularization term ... | SP:cfbe7ae1f40e2c23a6161d04e3229bc860c79042 |
Learning from Label Proportions with Consistency Regularization | 1 INTRODUCTION . In traditional supervised learning , a classifier is trained on a dataset where each instance is associated with a class label . However , label annotation can be expensive or difficult to obtain for some applications . Take the embryo selection as an example ( Hernández-González et al. , 2018 ) . To... | This paper proposes using Consistency Regularization and a new bag generation technique to better learn classification decision boundaries in a Label Proportion setting. The consistency regularization works to make sure that examples in the local neighbourhood have similar outputs. The authors further use K-means clus... | SP:cfbe7ae1f40e2c23a6161d04e3229bc860c79042 |
Adapting Behaviour for Learning Progress | 1 INTRODUCTION . Reinforcement learning ( RL ) is a general formalism modelling sequential decision making . It aspires to be broadly applicable , making minimal assumptions about the task at hand and reducing the need for prior knowledge . By learning behaviour from scratch , it has the potential to surpass human expe... | This papers studies how to explore, in order to generate experience for faster learning of policies in context of RL. RL methods typically employ simple hand-tuned exploration schedules (such as epsilon greedy exploration, and changing the epsilon as training proceeds). This paper proposes a scheme for learning this sc... | SP:54eb8cf5375f436952059b8e6890a0550b98fb52 |
Adapting Behaviour for Learning Progress | 1 INTRODUCTION . Reinforcement learning ( RL ) is a general formalism modelling sequential decision making . It aspires to be broadly applicable , making minimal assumptions about the task at hand and reducing the need for prior knowledge . By learning behaviour from scratch , it has the potential to surpass human expe... | This paper develops a multi-arm bandit-based algorithm to dynamically adapt the exploration policy for reinforcement learning. The arms of the bandit are parameters of the policy such as exploration noise, per-action biases etc. A proxy fitness metric is defined that measures the return of the trajectories upon perturb... | SP:54eb8cf5375f436952059b8e6890a0550b98fb52 |
How to 0wn the NAS in Your Spare Time | 1 INTRODUCTION . To continue outperforming state-of-the-art results , research in deep learning ( DL ) has shifted from manually engineering features to engineering DL systems , including novel data pre-processing pipelines ( Raff et al. , 2018 ; Wang et al. , 2019 ) and novel neural architectures ( Cai et al. , 2019 ;... | This paper proposes a way to attack and reconstruct a victim's neural architecture that is co-located on the same host. They do it through cache side-channel leakage and use Flush+Reload to extract the trace of victim's function call, which tells specific network operations. To recover the computational graph, they use... | SP:a8cb23a70671d54f8784ac023bbecbcbd0bffcfa |
How to 0wn the NAS in Your Spare Time | 1 INTRODUCTION . To continue outperforming state-of-the-art results , research in deep learning ( DL ) has shifted from manually engineering features to engineering DL systems , including novel data pre-processing pipelines ( Raff et al. , 2018 ; Wang et al. , 2019 ) and novel neural architectures ( Cai et al. , 2019 ;... | This work proposed a method to reconstruct machine learning pipelines and network architectures using cache side-channel attack. It is based on a previous proposed method Flush+Reload that generates the raw trace of function calls. Then the authors applied several techniques to rebuild the computational graph from the ... | SP:a8cb23a70671d54f8784ac023bbecbcbd0bffcfa |
Decoupling Representation and Classifier for Long-Tailed Recognition | 1 INTRODUCTION . Visual recognition research has made rapid advances during the past years , driven primarily by the use of deep convolutional neural networks ( CNNs ) and large image datasets , most importantly the ImageNet Challenge ( Russakovsky et al. , 2015 ) . Such datasets are usually artificially balanced with ... | The paper tries to handle the class imbalance problem by decoupling the learning process into representation learning and classification, in contrast to the current methods that jointly learn both of them. They comprehensively study several sampling methods for representation learning and different strategies for class... | SP:4fba557254310577845d291e0f216dc76403c9ac |
Decoupling Representation and Classifier for Long-Tailed Recognition | 1 INTRODUCTION . Visual recognition research has made rapid advances during the past years , driven primarily by the use of deep convolutional neural networks ( CNNs ) and large image datasets , most importantly the ImageNet Challenge ( Russakovsky et al. , 2015 ) . Such datasets are usually artificially balanced with ... | The paper considers the problem of long-tailed image classification, where the class frequencies during (supervised) training of an image classifier are heavily skewed, so that the classifier underfits on under-represented classes. Different known and novel sampling schemes during training as well as post-training proc... | SP:4fba557254310577845d291e0f216dc76403c9ac |
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations | 1 INTRODUCTION . Task execution in robotics and reinforcement learning ( RL ) requires accurate perception of and reasoning about discrete elements in an environment . While supervised methods can be used to identify pertinent objects , it is intractable to collect labels for every scenario and task . Discovering struc... | The paper proposes a generative model for images. There's a probability mask per-pixel per-component (which yields mixing probabilities), and then a set of latents per-component that yield an image. The system is tested on a set of scenes like the GQN dataset, stacks of blocks, and the multi-dsprites dataset. The syste... | SP:00fed729e27d8c9d2a3d96fdb7e54c3e5cc0a94d |
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations | 1 INTRODUCTION . Task execution in robotics and reinforcement learning ( RL ) requires accurate perception of and reasoning about discrete elements in an environment . While supervised methods can be used to identify pertinent objects , it is intractable to collect labels for every scenario and task . Discovering struc... | The authors propose a probabilistic generative latent variable model representing a 2D image as a mixture of latent components. It formulates the scene generation problem as a spatial Gaussian mixture model where each Gaussian component comes from the decoding of an object-centric latent variable. The contribution of t... | SP:00fed729e27d8c9d2a3d96fdb7e54c3e5cc0a94d |
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius | 1 INTRODUCTION . Modern neural network classifiers are able to achieve very high accuracy on image classification tasks but are sensitive to small , adversarially chosen perturbations to the inputs ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . Given an image x that is correctly classified by a neural network , a m... | This paper improves the robustness of smoothed classifiers by maximizing the certified radius, which is more efficient than adversarially train the smoothed classifier and achieves higher average robust radius and better certified robustness when the radius is not much larger than the training sigma. It proposes a nove... | SP:938f9b4e59217d2e78c405464b452ddc8ba5c459 |
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius | 1 INTRODUCTION . Modern neural network classifiers are able to achieve very high accuracy on image classification tasks but are sensitive to small , adversarially chosen perturbations to the inputs ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . Given an image x that is correctly classified by a neural network , a m... | This paper proposes a new approach to training models robust to perturbations (or 'attacks') within an l_2 radius, by maximizing a surrogate---a soft randomized smoothing loss---for the *certified radius* (a lower bound for the l_2 attack radius) of the classifier. This approach has the advantage of not needing to exp... | SP:938f9b4e59217d2e78c405464b452ddc8ba5c459 |
Variational Diffusion Autoencoders with Random Walk Sampling | Variational inference ( VI ) methods and especially variational autoencoders ( VAEs ) specify scalable generative models that enjoy an intuitive connection to manifold learning — with many default priors the posterior/likelihood pair q ( z|x ) /p ( x|z ) can be viewed as an approximate homeomorphism ( and its inverse )... | The paper studies the problem of density estimation and learning accurate generative models. The authors start from the observation that this problem has been approaches either using variational inference models, that scale very well but whose approximations may lead to degenerate results in practice, and diffusion map... | SP:820a879346c3ba370348f1086dab5b9c256175e9 |
Variational Diffusion Autoencoders with Random Walk Sampling | Variational inference ( VI ) methods and especially variational autoencoders ( VAEs ) specify scalable generative models that enjoy an intuitive connection to manifold learning — with many default priors the posterior/likelihood pair q ( z|x ) /p ( x|z ) can be viewed as an approximate homeomorphism ( and its inverse )... | The paper proposes a new generative model for unsupervised learning, based on a diffusion random walk principle inspired by the manifold learning literature. The basic idea is to (probabilistically) map points to a latent space, perform a random walk in that space, and then map back to the original space again. Learnin... | SP:820a879346c3ba370348f1086dab5b9c256175e9 |
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? | Recurrent neural networks ( RNNs ) are particularly well-suited for modeling longterm dependencies in sequential data , but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate . While a number of works attempt to mitigate this effect through gated re... | The authors present a novel work to address the problem of signal propagation in the recurrent neural networks. The idea is to build a attractor system for the signal transition from state h_{k-1} to h_k. If the attractor system converges to a equilibrium, then the hidden to hidden gradient is an identity matrix. This ... | SP:2f460faff6d62d462bd80a4545f3ce435d2ab0f6 |
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? | Recurrent neural networks ( RNNs ) are particularly well-suited for modeling longterm dependencies in sequential data , but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate . While a number of works attempt to mitigate this effect through gated re... | In this paper, the authors propose the incremental RNN (iRNN), which is inspired by the continuous-time RNN (CTRNN). Theoretically, the equilibrium point of iRNN exists and is unique. Furthermore, the norm of the Jacobian between two hidden states is always one, provided that the Euler iterations converge. The authors ... | SP:2f460faff6d62d462bd80a4545f3ce435d2ab0f6 |
Self-Supervised Learning of Appliance Usage | 1 INTRODUCTION . Learning home appliance usage patterns is useful for understanding user habits and optimizing electricity consumption . For example , knowing when a person uses their microwave , stove , oven , coffee machine or toaster provides information about their eating patterns . Similarly , understanding when t... | Authors proposed a multi-modal unsupervised algorithm to uncover the electricity usage of different appliances in a home. The detection of appliance was done by using both combined electricity consumption data and user location data from sensors. The unit of detection was set to be a 25-second window centered around an... | SP:cd9024c0331b487fcb0cc13872f3ddb01f57ce15 |
Self-Supervised Learning of Appliance Usage | 1 INTRODUCTION . Learning home appliance usage patterns is useful for understanding user habits and optimizing electricity consumption . For example , knowing when a person uses their microwave , stove , oven , coffee machine or toaster provides information about their eating patterns . Similarly , understanding when t... | This paper proposed a learning algorithm to recover the events of using an appliance and as well as the location of the appliance in a home by using smart electricity meter and a motion sensor installed a home. In the model, the input is a window of electricity energy consumption and context and the output of the model... | SP:cd9024c0331b487fcb0cc13872f3ddb01f57ce15 |
Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems | 1 INTRODUCTION . In this paper , we mainly consider the following composite convex optimization problem : min x∈Rd { P ( x ) def = F ( x ) +R ( x ) = 1 n n∑ i=1 fi ( x ) +R ( x ) } ( 1 ) where F ( x ) : Rd→R is the average of smooth convex component functions fi ( x ) , and R ( x ) is a relatively simple convex functio... | The paper proposes an optimization method for solving unconstrained convex optimization problems where the objective function consists of a sum of several smooth components f_i and a (not necessarily smooth) convex function R. The proposed method AVR-SExtraGD is a stochastic descent method building on the previous algo... | SP:99d5b859a30f1825f5a21fb62fdf7a918b838b95 |
Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems | 1 INTRODUCTION . In this paper , we mainly consider the following composite convex optimization problem : min x∈Rd { P ( x ) def = F ( x ) +R ( x ) = 1 n n∑ i=1 fi ( x ) +R ( x ) } ( 1 ) where F ( x ) : Rd→R is the average of smooth convex component functions fi ( x ) , and R ( x ) is a relatively simple convex functio... | This is an optimization algorithm paper, using the idea of "extragradient" and proposing to combine acceleration with proximal gradient descent-type algorithms (Prox-SVRG). Their proposed algorithm, i.e., accelerated variance reduced stochastic extra gradient descent, combines the advantages of Prox-SVRG and momentum a... | SP:99d5b859a30f1825f5a21fb62fdf7a918b838b95 |
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion | 1 INTRODUCTION . Multiple low-resolution images collectively contain more information than any individual lowresolution image , due to minor geometric displacements , e.g . shifts , rotations , atmospheric turbulence , and instrument noise . Multi-Frame Super-Resolution ( MFSR ) ( Tsai , 1984 ) aims to reconstruct hidd... | This paper proposes an end-to-end multi-frame super-resolution algorithm, that relies on a pair-wise co-registrations and fusing blocks (convolutional residual blocks), embedded in a encoder-decoder network 'HighRes-net' that estimates the super-resolution image. Because the ground truth SR image is typically misaligne... | SP:8357fc2c4234854bf476afd5305b1191ef56c11a |
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion | 1 INTRODUCTION . Multiple low-resolution images collectively contain more information than any individual lowresolution image , due to minor geometric displacements , e.g . shifts , rotations , atmospheric turbulence , and instrument noise . Multi-Frame Super-Resolution ( MFSR ) ( Tsai , 1984 ) aims to reconstruct hidd... | This paper presents a multi-frame super-resolution method applied to satellite imagery. It first estimates a reference image for the multiple input LR images by median filtering. Then it pairwise encodes the reference image and each of the multiple images in a recursive fashion then fuses the corresponding feature maps... | SP:8357fc2c4234854bf476afd5305b1191ef56c11a |
Improving End-to-End Object Tracking Using Relational Reasoning | 1 INTRODUCTION . Real-world environments can be rich and contain countless types of interacting objects . Intelligent autonomous agents need to understand both the objects and interactions between them if they are to operate in those environments . This motivates the need for class-agnostic algorithms for tracking mult... | The paper presents a class-agnostic method for tracking multiple moving objects (MOHART) that extends an existing single-object tracking method (Hierarchical Attentive Recurrent Tracking, HART). Similarly to HART, MOHART utilizes an attention mechanism and LSTM units. The extension form HART to MOHART is done in two ma... | SP:8719843b0fa8359a27642c1ffe94e17b748a0a60 |
Improving End-to-End Object Tracking Using Relational Reasoning | 1 INTRODUCTION . Real-world environments can be rich and contain countless types of interacting objects . Intelligent autonomous agents need to understand both the objects and interactions between them if they are to operate in those environments . This motivates the need for class-agnostic algorithms for tracking mult... | This paper deals with the problem of multiple object tracking and trajectory prediction in multiple frames of videos. The main focus is adding a relation-reasoning building block to the original HART framework. With multiple objects, the key is to be able to learn the permutation invariant representation during potenti... | SP:8719843b0fa8359a27642c1ffe94e17b748a0a60 |
A Deep Recurrent Neural Network via Unfolding Reweighted l1-l1 Minimization | 1 INTRODUCTION . The problem of reconstructing sequential signals from low-dimensional measurements across time is of great importance for a number of applications such as time-series data analysis , future-frame prediction , and compressive video sensing . Specifically , we consider the problem of reconstructing a seq... | This paper proposes a new reweighted-RNN by unfolding a reweighted L1-L1 minimization problem. It develops an iterative algorithm to solve the reweighted L1-L1 minimization problem, where the soft-thresholding functions can be adaptively learned. This paper provides the generalization error bound for deep RNNs and show... | SP:4605e601a717bc05833778d0916a393ffdf8c331 |
A Deep Recurrent Neural Network via Unfolding Reweighted l1-l1 Minimization | 1 INTRODUCTION . The problem of reconstructing sequential signals from low-dimensional measurements across time is of great importance for a number of applications such as time-series data analysis , future-frame prediction , and compressive video sensing . Specifically , we consider the problem of reconstructing a seq... | This paper proposes a novel method to solve the sequential signal reconstruction problem. The method is based on the deep unfolding methods and incorporates the reweighting mechanism. Additionally, they derive the generalization error bound and show how their over-parameterized reweighting RNNs ensure good generalizati... | SP:4605e601a717bc05833778d0916a393ffdf8c331 |
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks | 1 INTRODUCTION . Most methods for adversarial attacks on deep learning models operate in the so-called white-box setting ( Goodfellow et al. , 2014 ) , where the model being attacked , and its gradients , are assumed to be fully known . Recently , however there has also been considerable attention given to the black-bo... | In this paper, the authors propose a method for black box adversarial image generation. The idea is to learn a parameterization of a precision matrix so that gradients of a network's loss are assumed to be drawn from a corresponding Gaussian. The parameters of this model are fit efficiently using the spectral theorem t... | SP:896e4cb1fcd0dbbb9ecaa510dd5052721d46c68f |
Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks | 1 INTRODUCTION . Most methods for adversarial attacks on deep learning models operate in the so-called white-box setting ( Goodfellow et al. , 2014 ) , where the model being attacked , and its gradients , are assumed to be fully known . Recently , however there has also been considerable attention given to the black-bo... | This paper deals with the problem of finding an adversarial examples when only the output of a model can be evaluated, but not its gradient. The key idea of the paper is building a Gaussian MRF (a Gaussian with a sparse inverse covariance matrix with a special band structure) to maintain a model for the gradients for p... | SP:896e4cb1fcd0dbbb9ecaa510dd5052721d46c68f |
DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations | 1 INTRODUCTION . A main goal of cheminformatics in the area of drug discovery is to model the interaction of small molecules with proteins in-silico . The ability to accurately predict the binding affinity of a ligand towards a biological target without the need to conduct expensive in-vitro experiments has the potenti... | The authors present a model with state-of-the-art performance for predicting protein-ligand affinity and provide a thorough set of benchmarks to illustrate the superiority of combining learned low-dimensional embedding representations of both ligands and proteins. The authors then show that these learned representatio... | SP:62e72e469e4e6d1f0c6eac1074fd35439086e08a |
DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations | 1 INTRODUCTION . A main goal of cheminformatics in the area of drug discovery is to model the interaction of small molecules with proteins in-silico . The ability to accurately predict the binding affinity of a ligand towards a biological target without the need to conduct expensive in-vitro experiments has the potenti... | This paper tries to solve the protein-legend binding prediction problem in the computational biology field. It uses the learned embedding for protein and legend, separately, from two published papers. Then those two embeddings were inputted to another deep learning model, performing the final prediction. Tested on one ... | SP:62e72e469e4e6d1f0c6eac1074fd35439086e08a |
Removing input features via a generative model to explain their attributions to classifier's decisions | 1 INTRODUCTION . Explaining a classifier ’ s outputs given a certain input is increasingly important , especially for lifecritical applications ( Doshi-Velez & Kim , 2017 ) . A popular means for visually explaining an image classifier ’ s decisions is an attribution map i.e . a heatmap that highlights the input pixels ... | The paper proposes a deep visualization technique for black-box image classifiers that feeds modified versions of the original input by means of an off-the-shelf (black box too) image inpainting approach (DeepFill-v1), in order to capture changes in the classification performance. In particular, the substitution of the... | SP:6ef6e5580db4cfa041bd6a0063953dc52c29a2a5 |
Removing input features via a generative model to explain their attributions to classifier's decisions | 1 INTRODUCTION . Explaining a classifier ’ s outputs given a certain input is increasingly important , especially for lifecritical applications ( Doshi-Velez & Kim , 2017 ) . A popular means for visually explaining an image classifier ’ s decisions is an attribution map i.e . a heatmap that highlights the input pixels ... | The paper is focused on perturbation-based local explanation methods; methods that only need black-box(ish) access to the model and generally seek to find a region, pixel, etc's importance score through by removing that region, pixel, etc.. The intuition is that an important region if removed, will result in a large dr... | SP:6ef6e5580db4cfa041bd6a0063953dc52c29a2a5 |
Domain-Independent Dominance of Adaptive Methods | 1 INTRODUCTION . Deep network architectures are becoming increasingly complex , often containing parameters that can be grouped according to multiple functionalities , such as gating , attention , convolution , and generation . Such parameter groups should arguably be treated differently during training , as their grad... | In this paper the authors develop variants of Adam which corrects for the relationship of the gradient and adaptive terms that causes convergence issues, naming them Delayed Adam and AvaGrad. They also provide proofs demonstrating they solve the convergence issues of Adam in O(1/sqrt(T)) time. They also introduce a con... | SP:99b801c2541124ead1b8ff8904e7aa82c422c37c |
Domain-Independent Dominance of Adaptive Methods | 1 INTRODUCTION . Deep network architectures are becoming increasingly complex , often containing parameters that can be grouped according to multiple functionalities , such as gating , attention , convolution , and generation . Such parameter groups should arguably be treated differently during training , as their grad... | This paper proposes a new adaptive method, which is called AvaGrad. The authors first show that Adam may not converge to a stationary point for a stochastic convex optimization in Theorem1, which is closely related to [1]. They then show that by simply making $eta_t$ to be independent of the sample $s_t$, Adam is able ... | SP:99b801c2541124ead1b8ff8904e7aa82c422c37c |
Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks | 1 INTRODUCTION . Two standard approaches to deep learning for sequential image data are 3D Convolutional Neural Networks ( 3D CNNs ) , e.g . the I3D model ( Carreira & Zisserman ( 2017 ) ) , and recurrent neural networks ( RNNs ) . Among the RNNs , the convolutional long short-term memory network ( C-LSTM ) ( Shi et al... | The paper shows a way to compare what is learned by two very different networks trained for a video classification task. The two architectures are state-of-the-art methods, one relying on 3d-CNNs (time= one dimension), the other on conv-LSTMs (time is treated sequentially, using hidden states to pass information). The ... | SP:f99c39367808a1148a8b9559eef7d88cbccc8e6b |
Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks | 1 INTRODUCTION . Two standard approaches to deep learning for sequential image data are 3D Convolutional Neural Networks ( 3D CNNs ) , e.g . the I3D model ( Carreira & Zisserman ( 2017 ) ) , and recurrent neural networks ( RNNs ) . Among the RNNs , the convolutional long short-term memory network ( C-LSTM ) ( Shi et al... | This paper presents a paradigm for generating saliency maps for video models, specifically, I3D (3D CNN) and C-LSTM. It extends Fong & Vedaldi, 2017 to generate a temporal mask and introduces two types of "meaningful perturbations" for videos: freezing and reversing frames; they use Grad-CAM (with no modifications) for... | SP:f99c39367808a1148a8b9559eef7d88cbccc8e6b |
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings | 1 INTRODUCTION . Knowledge graph embedding ( KGE ) models learn algebraic representations , termed embeddings , of the entities and relations in a knowledge graph . They have been successfully applied to knowledge graph completion ( Nickel et al. , 2015 ) as well as in downstream tasks and applications such as recommen... | The paper presents an experimental study about some KGE methods. It argues that papers often propose changes in several different dimensions, such as model, loss, training, regularizer, etc., at once without providing a sufficient investigation about the individual components' contributions. The experimental study cons... | SP:9459318b83cfeeaf7ba7efa3b8a188977d9e572a |
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings | 1 INTRODUCTION . Knowledge graph embedding ( KGE ) models learn algebraic representations , termed embeddings , of the entities and relations in a knowledge graph . They have been successfully applied to knowledge graph completion ( Nickel et al. , 2015 ) as well as in downstream tasks and applications such as recommen... | The paper conducts a thorough analysis of existing models for constructing knowledge graph embeddings. It focuses on attempting to remove confounding aspects of model features and training regime, in order to better assess the merits of KGE models. The paper describes the reimplementation of five different KGE models, ... | SP:9459318b83cfeeaf7ba7efa3b8a188977d9e572a |
Unsupervised Intuitive Physics from Past Experiences | We consider the problem of learning models of intuitive physics from raw , unlabelled visual input . Differently from prior work , in addition to learning general physical principles , we are also interested in learning “ on the fly ” physical properties specific to new environments , based on a small number of environ... | The paper proposes an architecture for few-shot video prediction in which a number of videos are summarized through global pooling operations and passed into a video predictor that learns to leverage them for adaptation, similar in spirit to the RNN-based meta-learning approaches such as Santoro’16, Duan’16. Due to glo... | SP:e99ba3bc2e7d00a6d419b179cb76b5290b878f24 |
Unsupervised Intuitive Physics from Past Experiences | We consider the problem of learning models of intuitive physics from raw , unlabelled visual input . Differently from prior work , in addition to learning general physical principles , we are also interested in learning “ on the fly ” physical properties specific to new environments , based on a small number of environ... | The paper proposes a method to predict the future trajectory of a ball (or a set of balls) given the first few frames of the trajectory and a set of experience runs in the same environment. The model first learns to convert the set of images to a set of corresponding heatmaps that encode the location of the ball. Then,... | SP:e99ba3bc2e7d00a6d419b179cb76b5290b878f24 |
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery | Single Image Super Resolution ( SISR ) has significantly improved with Convolutional Neural Networks ( CNNs ) and Generative Adversarial Networks ( GANs ) , often achieving order of magnitude better pixelwise accuracies ( distortions ) and state-of-the-art perceptual accuracy . Due to the stochastic nature of GAN recon... | The paper considers the problem of generating a high-resolution image from a low-resolution one. The paper introduces the Grey Level Co-occurrence Matrix Method (GLCM) for evaluating the performance of super-resolution techniques and as an auxiliary loss function for training neural networks to perform well for super-r... | SP:6c766bf18a0b552410d411248af30915e331c5f7 |
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery | Single Image Super Resolution ( SISR ) has significantly improved with Convolutional Neural Networks ( CNNs ) and Generative Adversarial Networks ( GANs ) , often achieving order of magnitude better pixelwise accuracies ( distortions ) and state-of-the-art perceptual accuracy . Due to the stochastic nature of GAN recon... | This paper adopts a loss metric called Grey Level Co-occurence Matrix (GLCM) as a new measurement of perceptual quality for single image super-resolution. The GLCM is particularly well suited for automatic perceptual/textural in-domain comparisons, which does not require time-consuming expert MOS evaluations. Experimen... | SP:6c766bf18a0b552410d411248af30915e331c5f7 |
The Early Phase of Neural Network Training | 1 INTRODUCTION Over the past decade , methods for successfully training big , deep neural networks have revolutionized machine learning . Yet surprisingly , the underlying reasons for the success of these approaches remain poorly understood , despite remarkable empirical performance ( Santurkar et al. , 2018 ; Zhang et... | This paper aims at exploring the properties of neural network training during the early phase. By some studies on the lottery ticket hypothesis, something important happens during the early phase of training so rewinding the network should go to these early phases instead of the initial phase. So, what is important dur... | SP:2e7c43705291298211f8934cf38e84f8446d71ae |
The Early Phase of Neural Network Training | 1 INTRODUCTION Over the past decade , methods for successfully training big , deep neural networks have revolutionized machine learning . Yet surprisingly , the underlying reasons for the success of these approaches remain poorly understood , despite remarkable empirical performance ( Santurkar et al. , 2018 ; Zhang et... | This paper is dedicated to examining the changes that networks undergo during the early phase of the network training. The author conducts extensive measurements of the network state and its updates during the early iterations of training. Based on the observations, they find that: i) deep network is not robust to rein... | SP:2e7c43705291298211f8934cf38e84f8446d71ae |
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents | 1 INTRODUCTION . Humans can naturally learn and perform well at a wide variety of tasks , driven by instinct and practice ; more importantly , they are able to justify why they would take a certain action . Artificial agents should be equipped with the same capability , so that their decision making process is interpre... | This paper proposes a generative technique to sample "interesting" states useful for analyzing the behavior of deep reinforcement learning agents. In this context, the concept of "interesting" is defined via user-specific target functions, e.g. states that arise as a consequence of taking specific actions (such as acti... | SP:3ae544b075487fc2a3731e1c017546ef0ff525e9 |
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents | 1 INTRODUCTION . Humans can naturally learn and perform well at a wide variety of tasks , driven by instinct and practice ; more importantly , they are able to justify why they would take a certain action . Artificial agents should be equipped with the same capability , so that their decision making process is interpre... | This paper proposes a new visualization tool in order to understand the behavior of agents trained using deep RL. Specifically, they train a generative model of game states, and then optimize an energy-based distribution over state embeddings according to some target function, and then by sampling from the resulting di... | SP:3ae544b075487fc2a3731e1c017546ef0ff525e9 |
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | 1 INTRODUCTION . Few-shot learning refers to learning new concepts from few examples , an ability that humans naturally possess , but machines still lack . Improving on this aspect would lead to more efficient algorithms that can flexibly expand their knowledge without requiring large labeled datasets . We focus on few... | The authors of this paper construct a new few-shot learning dataset. The whole dataset consists of several data from different sources. The authors test several representative meta-learning models (e.g., matching network, Prototype network, MAML) on this dataset and give the analysis. Furthermore, the authors combine M... | SP:61bb7f39ffbf7caed41c8c0ef0650010d8a253aa |
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | 1 INTRODUCTION . Few-shot learning refers to learning new concepts from few examples , an ability that humans naturally possess , but machines still lack . Improving on this aspect would lead to more efficient algorithms that can flexibly expand their knowledge without requiring large labeled datasets . We focus on few... | The paper presents Meta-Dataset, a benchmark for few-shot classification that combines various image classification data sets, allows the number of classes and examples per class to vary, and considers the relationships between classes. It performs an empirical evaluation of six algorithms from the literature, k-NN, Fi... | SP:61bb7f39ffbf7caed41c8c0ef0650010d8a253aa |
Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning | 1 INTRODUCTION . The vast majority of data in the world can be thought of as created by unknown , and possibly complex , normal behavior of data generating systems . But what happens when data is generated by an alternative system instead ? Fraudulent records , disease outbreaks , cancerous cells on pathology slides , ... | The paper proposed a scheme to detect the presence of anomalous inputs, such as samples designed adversarially for deep learning tasks, that is based on a "subset scanning" approach to detect anomalous activations in the deep learning network. The paper is considering a very interesting problem and provides the suitabl... | SP:be873fc103ccbf8312a711d8798834c2d824438b |
Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning | 1 INTRODUCTION . The vast majority of data in the world can be thought of as created by unknown , and possibly complex , normal behavior of data generating systems . But what happens when data is generated by an alternative system instead ? Fraudulent records , disease outbreaks , cancerous cells on pathology slides , ... | The paper is the first paper, in my knowledge, that introduces the problem of identifying anomalous (or corrupted) subset of data input to a neural network. The corrupted inputs are identified vis-a-vis a set of “clean” background set (e.g., the training/ validation data set). Experimental evaluation is performed on th... | SP:be873fc103ccbf8312a711d8798834c2d824438b |
An Exponential Learning Rate Schedule for Deep Learning | • Training can be done using SGD with momentum and an exponentially increasing learning rate schedule , i.e. , learning rate increases by some ( 1 + α ) factor in every epoch for some α > 0 . ( Precise statement in the paper . ) To the best of our knowledge this is the first time such a rate schedule has been successfu... | This work makes an interesting observation that it is possible to use exponentially growing learning rate schedule when training with neural networks with batch normalization. This paper provides both theoretical insights and empirical demonstration of this remarkable property. In detail, the authors prove that for sto... | SP:b619dae0690930ba616bfeb3e32e89de6e798993 |
An Exponential Learning Rate Schedule for Deep Learning | • Training can be done using SGD with momentum and an exponentially increasing learning rate schedule , i.e. , learning rate increases by some ( 1 + α ) factor in every epoch for some α > 0 . ( Precise statement in the paper . ) To the best of our knowledge this is the first time such a rate schedule has been successfu... | This exciting and insightful paper presents theorems (and illustrating examples and experiments) describing an equivalence of commonly used learning rate schedules and weight decay settings with an exponentially increasing learning rate schedule and no weight decay, for neural networks with scale-invariant weights. Hen... | SP:b619dae0690930ba616bfeb3e32e89de6e798993 |
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