|
Poster
|
Tue 9:00
|
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
|
|
|
Poster
|
Thu 1:00
|
The inductive bias of ReLU networks on orthogonally separable data
Mary Phuong · Christoph H Lampert
|
|
|
Poster
|
Mon 17:00
|
On the geometry of generalization and memorization in deep neural networks
Cory Stephenson · Suchismita Padhy · Abhinav Ganesh · Yue Hui · Hanlin Tang · SueYeon Chung
|
|
|
Poster
|
Tue 17:00
|
A unifying view on implicit bias in training linear neural networks
Chulhee Yun · Shankar Krishnan · Hossein Mobahi
|
|
|
Poster
|
Tue 9:00
|
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu · Difan Zou · vladimir braverman · Quanquan Gu
|
|
|
Poster
|
Mon 17:00
|
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Taiji Suzuki · Akiyama Shunta
|
|
|
Poster
|
Tue 17:00
|
Understanding the role of importance weighting for deep learning
Da Xu · Yuting Ye · Chuanwei Ruan
|
|
|
Poster
|
Tue 1:00
|
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent
El Mahdi El Mhamdi · Rachid Guerraoui · Sébastien Rouault
|
|
|
Poster
|
Thu 17:00
|
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Zixiang Chen · Yuan Cao · Difan Zou · Quanquan Gu
|
|
|
Poster
|
Thu 1:00
|
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima
Zeke Xie · Issei Sato · Masashi Sugiyama
|
|
|
Poster
|
Thu 1:00
|
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Atsushi Nitanda · Taiji Suzuki
|
|
|
Poster
|
Tue 17:00
|
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
Jeremy Cohen · Simran Kaur · Yuanzhi Li · Zico Kolter · Ameet Talwalkar
|
|
