15 citations · 18 across the 12 of their papers we have counts for
5 papers · 1 filter
FastMix: Fast Data Mixture Optimization via Gradient Descent
Haoru Tan, Sitong Wu, Yanfeng Chen +5
While large and diverse datasets have driven recent advances in large models, identifying the optimal data mixture for pre-training and post-training remains a significant open pro…
FairLRF: Achieving Fairness through Sparse Low Rank Factorization
Yuanbo Guo, Jun Xia, Yiyu Shi
As deep learning (DL) techniques become integral to various applications, ensuring model fairness while maintaining high performance has become increasingly critical, particularly…
Enabling On-Device Learning via Experience Replay with Efficient Dataset Condensation
Gelei Xu, Ningzhi Tang, Jun Xia +2
Upon deployment to edge devices, it is often desirable for a model to further learn from streaming data to improve accuracy. However, extracting representative features from such d…
Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client
Jun Xia, Yi Zhang, Yiyu Shi
Although Federated Learning (FL) is promising in knowledge sharing for heterogeneous Artificial Intelligence of Thing (AIoT) devices, their training performance and energy efficacy…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…