9 citations · 29 across the 8 of their papers we have counts for
8 papers
Global Convergence in Neural ODEs: Impact of Activation Functions
Tianxiang Gao, Siyuan Sun, Hailiang Liu +1
Neural Ordinary Differential Equations (ODEs) have been successful in various applications due to their continuous nature and parameter-sharing efficiency. However, these unique ch…
Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models
Tianxiang Gao, Xiaokai Huo, Hailiang Liu +1
Neural networks with wide layers have attracted significant attention due to their equivalence to Gaussian processes, enabling perfect fitting of training data while maintaining ge…
On the optimization and generalization of overparameterized implicit neural networks
Tianxiang Gao, Hongyang Gao
Implicit neural networks have become increasingly attractive in the machine learning community since they can achieve competitive performance but use much less computational resour…
Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths
Tianxiang Gao, Hongyang Gao
Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep netw…
A global convergence theory for deep ReLU implicit networks via over-parameterization
Tianxiang Gao, Hailiang Liu, Jia Liu +2
Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectu…
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization
Tianxiang Gao, Songtao Lu, Jia Liu +1
We propose a new \textit{randomized Bregman (block) coordinate descent} (RBCD) method for minimizing a composite problem, where the objective function could be either convex or non…
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems
Tianxiang Gao, Songtao Lu, Jia Liu +1
In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continu…
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization
Tianxiang Gao, Chris Chu
Nonnegative matrix factorization (NMF) has attracted much attention in the last decade as a dimension reduction method in many applications. Due to the explosion in the size of dat…