2 citations · 2 across the 12 of their papers we have counts for
4 papers · 1 filter
Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
Junyi Yao, Parham Eftekhar, Gene Cheung +3
Samples of brain signals collected by EEG sensors have inherent anti-correlations that are well modeled by negative edges in a finite graph. To differentiate epilepsy patients from…
Provable Low-Frequency Bias of In-Context Learning of Representations
Yongyi Yang, Hidenori Tanaka, Wei Hu
In-context learning (ICL) enables large language models (LLMs) to acquire new behaviors from the input sequence alone without any parameter updates. Recent studies have shown that…
What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers
Pulkit Gopalani, Wei Hu
Training Transformers on algorithmic tasks frequently demonstrates an intriguing abrupt learning phenomenon: an extended performance plateau followed by a sudden, sharp improvement…
RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm
Yongyi Yang, Jianyang Gao, Wei Hu
Post-training Quantization (PTQ) has become a widely used technique for improving inference efficiency of large language models (LLMs). However, existing PTQ methods generally suff…