306 citations · 682 across the 25 of their papers we have counts for
5 papers · 1 filter
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence
Chengyue Gong, Dilin Wang, Qiang Liu
Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficien…
Splitting Steepest Descent for Growing Neural Architectures
Qiang Liu, Lemeng Wu, Dilin Wang
We develop a progressive training approach for neural networks which adaptively grows the network structure by splitting existing neurons to multiple off-springs. By leveraging a f…
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent
Dilin Wang, Meng Li, Lemeng Wu +2
Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are h…
Improving Neural Language Modeling via Adversarial Training
Dilin Wang, Chengyue Gong, Qiang Liu
Recently, substantial progress has been made in language modeling by using deep neural networks. However, in practice, large scale neural language models have been shown to be pron…
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang, Hao Liu, Qiang Liu
Variational inference with α-divergences has been widely used in modern probabilistic machine learning. Compared to Kullback-Leibler (KL) divergence, a major advantage of using α-d…