7 papers
CageViT: Convolutional Activation Guided Efficient Vision Transformer
Hao Zheng, Jinbao Wang, Xiantong Zhen +3
Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input…
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective
Yuxin Dong, Tieliang Gong, Hong Chen +1
Recently, information theoretic analysis has become a popular framework for understanding the generalization behavior of deep neural networks. It allows a direct analysis for stoch…
Stepdown SLOPE for Controlled Feature Selection
Jingxuan Liang, Hong Chen, Xuelin Zhang +2
Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical property as well as empirical behavior recently on the false discovery rate (FDR) control of high-dimension…
Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning
Jiahuan Wang, Jun Chen, Hong Chen +3
Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of "pointwise loss + pairwise loss" and have sh…
On the Stability and Generalization of Triplet Learning
Jun Chen, Hong Chen, Xue Jiang +4
Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and pe…
Error-based Knockoffs Inference for Controlled Feature Selection
Xuebin Zhao, Hong Chen, Yingjie Wang +4
Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the p…
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
Hongjun Chen, Jinbao Wang, Hong Cai Chen +4
Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to…