most citedHierarchical Attention Network for Action Recognition in Videos

78 citations · 89 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20241 cited

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective

Shenglai Zeng, Jiankun Zhang, Bingheng Li +8

Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…

cs.LG2024

Masked Graph Autoencoder with Non-discrete Bandwidths

Ziwen Zhao, Yuhua Li, Yixiong Zou +2

Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete ed…

cs.LG20242 cited

Superiority of Multi-Head Attention in In-Context Linear Regression

Yingqian Cui, Jie Ren, Pengfei He +2

We present a theoretical analysis of the performance of transformer with softmax attention in in-context learning with linear regression tasks. While the existing literature predom…

stat.ML20169 cited

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10

Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…

cs.CV201678 cited

Hierarchical Attention Network for Action Recognition in Videos

Yilin Wang, Suhang Wang, Jiliang Tang +3

Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (…