605 citations · 1.2k across the 29 of their papers we have counts for
6 papers · 1 filter
Visualizing the Loss Landscape of Neural Nets
Hao Li, Zheng Xu, Gavin Taylor +2
Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip…
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Feiyun Zhu, Jun Guo, Zheng Xu +2
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health. State-…
Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu, Gavin Taylor, Hao Li +3
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined…
Training Quantized Nets: A Deeper Understanding
Hao Li, Soham De, Zheng Xu +3
Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-p…
Stabilizing Adversarial Nets With Prediction Methods
Abhay Yadav, Sohil Shah, Zheng Xu +2
Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train. These difficulties come from the fact that optimal weights for ad…
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan +2
Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. T…