94 citations · 102 across the 5 of their papers we have counts for
14 papers
Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation
Wenjing Wang, Zhengbo Xu, Haofeng Huang +1
Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light…
Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain
Ji Liu, Zheng Xu, Yanmei Zhang +3
Since the emergence of blockchain technology, its application in the financial market has always been an area of focus and exploration by all parties. With the characteristics of a…
Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training
Zheng Xu, Ali Shafahi, Tom Goldstein
Adversarial training has proven to be effective in hardening networks against adversarial examples. However, the gained robustness is limited by network capacity and number of trai…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi +6
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, t…
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent
Karthik A. Sankararaman, Soham De, Zheng Xu +2
This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradie…