
Mastering Atari with Discrete World Models
Intelligent agents need to generalize from past experience to achieve go...
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Action and Perception as Divergence Minimization
We introduce a unified objective for action and perception of intelligen...
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A Study of Gradient Variance in Deep Learning
The impact of gradient noise on training deep models is widely acknowled...
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The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
In this work, we focus on an analogical reasoning task that contains ric...
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INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
In learningassisted theorem proving, one of the most critical challenge...
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Maximum Entropy Gain Exploration for Long Horizon Multigoal Reinforcement Learning
What goals should a multigoal reinforcement learning agent pursue durin...
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When Does Preconditioning Help or Hurt Generalization?
While second order optimizers such as natural gradient descent (NGD) oft...
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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Ensembles, where multiple neural networks are trained individually and t...
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An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Distances are pervasive in machine learning. They serve as similarity me...
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Dream to Control: Learning Behaviors by Latent Imagination
Learned world models summarize an agent's experience to facilitate learn...
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On Solving Minimax Optimization Locally: A FollowtheRidge Approach
Many tasks in modern machine learning can be formulated as finding equil...
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Lookahead Optimizer: k steps forward, 1 step back
The vast majority of successful deep neural networks are trained using v...
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Benchmarking ModelBased Reinforcement Learning
Modelbased reinforcement learning (MBRL) is widely seen as having the p...
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Exploring Modelbased Planning with Policy Networks
Modelbased reinforcement learning (MBRL) with modelpredictive control ...
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Neural Graph Evolution: Towards Efficient Automatic Robot Design
Despite the recent successes in robotic locomotion control, the design o...
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Graph Normalizing Flows
We introduce graph normalizing flows: a new, reversible graph neural net...
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Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise
The choice of batchsize in a stochastic optimization algorithm plays a ...
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DOMQNET: Grounded RL on Structured Language
Building agents to interact with the web would allow for significant imp...
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ACTRCE: Augmenting Experience via Teacher's Advice For MultiGoal Reinforcement Learning
Sparse reward is one of the most challenging problems in reinforcement l...
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Reversible Recurrent Neural Networks
Recurrent neural networks (RNNs) provide stateoftheart performance in...
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Flipout: Efficient PseudoIndependent Weight Perturbations on MiniBatches
Stochastic neural net weights are used in a variety of contexts, includi...
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Solving Approximate Wasserstein GANs to Stationarity
Generative Adversarial Networks (GANs) are one of the most practical str...
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Scalable trustregion method for deep reinforcement learning using Kroneckerfactored approximation
In this work, we propose to apply trust region optimization to deep rein...
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Using Fast Weights to Attend to the Recent Past
Until recently, research on artificial neural networks was largely restr...
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Learning WakeSleep Recurrent Attention Models
Despite their success, convolutional neural networks are computationally...
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Predicting Deep ZeroShot Convolutional Neural Networks using Textual Descriptions
One of the main challenges in ZeroShot Learning of visual categories is...
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Inspired by recent work in machine translation and object detection, we ...
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Multiple Object Recognition with Visual Attention
We present an attentionbased model for recognizing multiple objects in ...
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Jimmy Ba
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