|
Workshop
|
|
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
Marc Vischer · Henning Sprekeler · Robert Lange
|
|
|
Poster
|
Thu 17:00
|
Adapting to Reward Progressivity via Spectral Reinforcement Learning
Michael Dann · John Thangarajah
|
|
|
Poster
|
Thu 9:00
|
Deconstructing the Regularization of BatchNorm
Yann Dauphin · Ekin Cubuk
|
|
|
Workshop
|
Fri 5:15
|
Geometric Deep Learning
Fernando Gama
|
|
|
Poster
|
Thu 17:00
|
Neural Thompson Sampling
Weitong ZHANG · Dongruo Zhou · Lihong Li · Quanquan Gu
|
|
|
Workshop
|
Fri 11:00
|
Keynote 6: Liangwei Ge. Title: Deep learning challenges and how Intel is addressing them
|
|
|
Workshop
|
Fri 11:52
|
DeepSMOTE: Deep Learning for Imbalanced Data
Bartosz Krawczyk
|
|
|
Poster
|
Thu 17:00
|
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei · Kendrick Shen · Yining Chen · Tengyu Ma
|
|
|
Oral
|
Thu 19:00
|
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei · Kendrick Shen · Yining Chen · Tengyu Ma
|
|
|
Spotlight
|
Wed 13:58
|
Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer · Dan Boneh
|
|
|
Poster
|
Wed 17:00
|
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
Lin Ning · Guoyang Chen · Weifeng Zhang · Xipeng Shen
|
|
|
Poster
|
Wed 9:00
|
Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan · Pedro Savarese · Michael Maire
|
|
