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Workshop
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Fri 14:02
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Invited Talk: A deep learning theory for neural networks grounded in physics
Benjamin Scellier
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Poster
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Thu 9:00
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Deconstructing the Regularization of BatchNorm
Yann Dauphin · Ekin Cubuk
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Poster
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Thu 17:00
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Adapting to Reward Progressivity via Spectral Reinforcement Learning
Michael Dann · John Thangarajah
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Poster
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Thu 17:00
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Neural Thompson Sampling
Weitong ZHANG · Dongruo Zhou · Lihong Li · Quanquan Gu
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Workshop
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Fri 11:52
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DeepSMOTE: Deep Learning for Imbalanced Data
Bartosz Krawczyk
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Workshop
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Fri 11:00
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Keynote 6: Liangwei Ge. Title: Deep learning challenges and how Intel is addressing them
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Workshop
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Fri 5:15
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Geometric Deep Learning
Fernando Gama
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Poster
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Thu 17:00
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Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei · Kendrick Shen · Yining Chen · Tengyu Ma
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Oral
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Thu 19:00
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Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei · Kendrick Shen · Yining Chen · Tengyu Ma
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Poster
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Wed 9:00
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Modeling the Second Player in Distributionally Robust Optimization
Paul Michel · Tatsunori Hashimoto · Graham Neubig
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Spotlight
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Wed 13:58
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Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer · Dan Boneh
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Poster
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Wed 9:00
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Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan · Pedro Savarese · Michael Maire
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