18 citations · 22 across the 4 of their papers we have counts for
4 papers
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Jingge Wang, Yang Li, Liyan Xie +1
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain. In this work, we focus on the do…
Dep-: Improving -based Network Sparsification via Dependency Modeling
Yang Li, Shihao Ji
Training deep neural networks with an regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training b…
Joint Text and Label Generation for Spoken Language Understanding
Yang Li, Ben Athiwaratkun, Cicero Nogueira dos Santos +1
Generalization is a central problem in machine learning, especially when data is limited. Using prior information to enforce constraints is the principled way of encouraging genera…
-ARM: Network Sparsification via Stochastic Binary Optimization
Yang Li, Shihao Ji
We consider network sparsification as an -norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attache…