|
Poster
|
Wed 9:00
|
Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan · Pedro Savarese · Michael Maire
|
|
|
Poster
|
Thu 17:00
|
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei · Kendrick Shen · Yining Chen · Tengyu Ma
|
|
|
Poster
|
Wed 9:00
|
Modeling the Second Player in Distributionally Robust Optimization
Paul Michel · Tatsunori Hashimoto · Graham Neubig
|
|
|
Workshop
|
Fri 14:25
|
Invited Speaker Lu Jiang - Robust Deep Learning and Applications
Lu Jiang
|
|
|
Oral
|
Mon 11:30
|
Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan · Pedro Savarese · Michael Maire
|
|
|
Poster
|
Wed 17:00
|
Learning Manifold Patch-Based Representations of Man-Made Shapes
Dmitriy Smirnov · Mikhail Bessmeltsev · Justin Solomon
|
|
|
Invited Talk
|
Tue 0:00
|
Geometric Deep Learning: the Erlangen Programme of ML
Michael Bronstein
|
|
|
Workshop
|
Fri 7:00
|
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective
Yan Leng
|
|
|
Oral
|
Tue 19:55
|
Global Convergence of Three-layer Neural Networks in the Mean Field Regime
Huy Tuan Pham · Phan-Minh Nguyen
|
|
|
Workshop
|
Fri 6:00
|
Keynote 3: Ehsan Saboori. Title: Deep learning model compression using neural network design space exploration
|
|
|
Poster
|
Wed 1:00
|
Neural ODE Processes
Alexander Norcliffe · Cristian Bodnar · Ben Day · Jacob Moss · Pietro Liò
|
|
|
Poster
|
Tue 17:00
|
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
Thao Nguyen · Maithra Raghu · Simon Kornblith
|
|
