54 citations · 119 across the 16 of their papers we have counts for
16 papers
Causal-DFQ: Causality Guided Data-free Network Quantization
Yuzhang Shang, Bingxin Xu, Gaowen Liu +2
Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devi…
Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition
Jianyuan Ni, Hao Tang, Anne H. H. Ngu +2
Wearable sensor-based Human Action Recognition (HAR) has made significant strides in recent times. However, the accuracy performance of wearable sensor-based HAR is currently still…
Optical Flow Estimation in 360 Videos: Dataset, Model and Application
Bin Duan, Keshav Bhandari, Gaowen Liu +1
Optical flow estimation has been a long-lasting and fundamental problem in the computer vision community. However, despite the advances of optical flow estimation in perspective vi…
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Zhuoning Yuan, Yan Yan, Milan Sonka +1
Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focu…
Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise
Yan Yan, Xin Man, Tianbao Yang
In this paper, we propose robust stochastic algorithms for solving convex compositional problems of the form $f(\E_ξg(\cdot; ξ)) + r(\cdot)$ by establishing {\bf sub-Gaussian confi…
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
Zhishuai Guo, Yan Yan, Zhuoning Yuan +1
This paper focuses on stochastic methods for solving smooth non-convex strongly-concave min-max problems, which have received increasing attention due to their potential applicatio…