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Meta-Learning to Improve Pre-Training



Aniruddh Raghu , Jonathan Lorraine , Simon Kornblith , Matthew McDermott , David Duvenaud

* NeurIPS 2021 

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Complex Momentum for Learning in Games



Jonathan Lorraine , David Acuna , Paul Vicol , David Duvenaud


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Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations



Winnie Xu , Ricky T. Q. Chen , Xuechen Li , David Duvenaud


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Oops I Took A Gradient: Scalable Sampling for Discrete Distributions



Will Grathwohl , Kevin Swersky , Milad Hashemi , David Duvenaud , Chris J. Maddison

* Energy-Based Models, Deep generative models, MCMC sampling 

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Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering



Ricky T. Q. Chen , Dami Choi , Lukas Balles , David Duvenaud , Philipp Hennig


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Teaching with Commentaries



Aniruddh Raghu , Maithra Raghu , Simon Kornblith , David Duvenaud , Geoffrey Hinton


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No MCMC for me: Amortized sampling for fast and stable training of energy-based models



Will Grathwohl , Jacob Kelly , Milad Hashemi , Mohammad Norouzi , Kevin Swersky , David Duvenaud


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A Study of Gradient Variance in Deep Learning



Fartash Faghri , David Duvenaud , David J. Fleet , Jimmy Ba


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Learning Differential Equations that are Easy to Solve



Jacob Kelly , Jesse Bettencourt , Matthew James Johnson , David Duvenaud


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