83.4k citations · 96.3k across the 51 of their papers we have counts for
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Planning from Pixels using Inverse Dynamics Models
Keiran Paster, Sheila A. McIlraith, Jimmy Ba
Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by l…
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Jingling Li, Mozhi Zhang, Keyulu Xu +2
Noisy labels are inevitable in large real-world datasets. In this work, we explore an area understudied by previous works -- how the network's architecture impacts its robustness t…
Mastering Atari with Discrete World Models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi +1
Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from i…
Action and Perception as Divergence Minimization
Danijar Hafner, Pedro A. Ortega, Jimmy Ba +3
To learn directed behaviors in complex environments, intelligent agents need to optimize objective functions. Various objectives are known for designing artificial agents, includin…
A Study of Gradient Variance in Deep Learning
Fartash Faghri, David Duvenaud, David J. Fleet +1
The impact of gradient noise on training deep models is widely acknowledged but not well understood. In this context, we study the distribution of gradients during training. We int…
The Scattering Compositional Learner: Discovering Objects, Attributes, Relationships in Analogical Reasoning
Yuhuai Wu, Honghua Dong, Roger Grosse +1
In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM). To discover compositional structures of the…
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao +2
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a u…
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba +1
In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequ…
When Does Preconditioning Help or Hurt Generalization?
Shun-ichi Amari, Jimmy Ba, Roger Grosse +5
While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a…
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen, Dustin Tran, Jimmy Ba
Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and p…
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Silviu Pitis, Harris Chan, Kiarash Jamali +1
Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When de…