7.3k citations · 21.3k across the 74 of their papers we have counts for
13 papers · 1 filter
WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen, Yu Zhang, Heiga Zen +3
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and di…
Why Do Better Loss Functions Lead to Less Transferable Features?
Simon Kornblith, Ting Chen, Honglak Lee +1
Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions…
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Will Grathwohl, Jacob Kelly, Milad Hashemi +3
Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging prob…
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…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…
Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky +2
One paradigm for learning from few labeled examples while making best use of a large amount of unlabeled data is unsupervised pretraining followed by supervised fine-tuning. Althou…