activity
20162026
most citedUnderstanding Top-k Sparsification in Distributed Deep Learning

67 citations · 307 across the 37 of their papers we have counts for

collaborators

38 papers

math.NA2026

Deep-Learning Solvers and Surrogates for Infinity and p-Laplace Problems

Tak Shing Au Yeung, Ka Chun Cheung, Hannah Potgieter +2

We investigate the use of neural network solvers for infinity and -Laplace problems, which are fundamental in nonlinear analysis and have practical applications. Our approach em…

cs.CL2023★ 3 cited

Self-Consistent Narrative Prompts on Abductive Natural Language Inference

Chunkit Chan, Xin Liu, Tsz Ho Chan +4

Abduction has long been seen as crucial for narrative comprehension and reasoning about everyday situations. The abductive natural language inference (NLI) task has been propose…

cs.CV2023★ 2 cited

Learning Gabor Texture Features for Fine-Grained Recognition

Lanyun Zhu, Tianrun Chen, Jianxiong Yin +2

Extracting and using class-discriminative features is critical for fine-grained recognition. Existing works have demonstrated the possibility of applying deep CNNs to exploit featu…

cs.SE2023★ 41 cited

Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

Zhehua Zhou, Jiayang Song, Xuan Xie +5

As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes, indicating its potential to act…

cs.CV2023★ 3 cited

CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

Ziyuan Luo, Qing Guo, Ka Chun Cheung +2

Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright sho…

cs.MA2023

A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

Wei-Fang Sun, Cheng-Kuang Lee, Simon See +1

In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously chan…