80 citations · 343 across the 26 of their papers we have counts for
11 papers · 1 filter
Incremental Learning via Rate Reduction
Ziyang Wu, Christina Baek, Chong You +1
Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The…
Comments on Efficient Singular Value Thresholding Computation
Zhengyuan Zhou, Yi Ma
We discuss how to evaluate the proximal operator of a convex and increasing function of a nuclear norm, which forms the key computational step in several first-order optimization a…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients
Yifei Huang, Yaodong Yu, Hongyang Zhang +2
In this paper we introduce a provably stable architecture for Neural Ordinary Differential Equations (ODEs) which achieves non-trivial adversarial robustness under white-box advers…
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
Yichao Zhou, Jingwei Huang, Xili Dai +4
We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution that are acc…
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
Haozhi Qi, Xiaolong Wang, Deepak Pathak +2
Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions…