28 citations · 29 across the 23 of their papers we have counts for
6 papers · 1 filter
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques +19
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Exi…
Localizing and Editing Knowledge in Large Audio-Language Models
Sung Kyun Chung, Jiaheng Dong, Qiuchi Hu +3
Large Audio-Language Models (LALMs) have shown strong performance in speech understanding, making speech a natural interface for accessing factual information. Yet they are trained…
Rethinking Perplexity: Revealing the Impact of Input Length on Perplexity Evaluation in LLMs
Letian Cheng, Junyan Wang, Yan Gao +3
Perplexity is a widely adopted metric for assessing the predictive quality of large language models (LLMs) and often serves as a reference metric for downstream evaluations. Howeve…
AdaNODEs: Test Time Adaptation for Time Series Forecasting Using Neural ODEs
Ting Dang, Soumyajit Chatterjee, Hong Jia +3
Test time adaptation (TTA) has emerged as a promising solution to adapt pre-trained models to new, unseen data distributions using unlabeled target domain data. However, most TTA m…
E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Models
Jiaheng Dong, Hong Jia, Soumyajit Chatterjee +3
Speech Foundation Models encounter significant performance degradation when deployed in real-world scenarios involving acoustic domain shifts, such as background noise and speaker…
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
Yu Wu, Ting Dang, Dimitris Spathis +2
Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. H…