11 citations · 15 across the 7 of their papers we have counts for
7 papers
Matching Skeleton-based Activity Representations with Heterogeneous Signals for HAR
Shuheng Li, Jiayun Zhang, Xiaohan Fu +3
In human activity recognition (HAR), activity labels have typically been encoded in one-hot format, which has a recent shift towards using textual representations to provide contex…
Orthogonal Calibration for Asynchronous Federated Learning
Jiayun Zhang, Shuheng Li, Haiyu Huang +3
Asynchronous federated learning mitigates the inefficiency of conventional synchronous aggregation by integrating updates as they arrive and adjusting their influence based on stal…
Creating a Cooperative AI Policymaking Platform through Open Source Collaboration
Aiden Lewington, Alekhya Vittalam, Anshumaan Singh +48
Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Curre…
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Chenyang An, Zhibo Chen, Qihao Ye +6
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof s…
Physics-Informed Data Denoising for Real-Life Sensing Systems
Xiyuan Zhang, Xiaohan Fu, Diyan Teng +9
Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reli…
Unleashing the Power of Shared Label Structures for Human Activity Recognition
Xiyuan Zhang, Ranak Roy Chowdhury, Jiayun Zhang +3
Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different…
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang +3
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set…