47 citations · 109 across the 11 of their papers we have counts for
4 papers · 1 filter
CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
Chi-hsuan Wu, Shih-yang Liu, Xijie Huang +6
Online learning is a rapidly growing industry. However, a major doubt about online learning is whether students are as engaged as they are in face-to-face classes. An engagement re…
LLM-FP4: 4-Bit Floating-Point Quantized Transformers
Shih-yang Liu, Zechun Liu, Xijie Huang +2
We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-trainin…
Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection
Xijie Huang, Zechun Liu, Shih-Yang Liu +1
Quantization-aware training (QAT) is a representative model compression method to reduce redundancy in weights and activations. However, most existing QAT methods require end-to-en…
Oscillation-free Quantization for Low-bit Vision Transformers
Shih-Yang Liu, Zechun Liu, Kwang-Ting Cheng
Weight oscillation is an undesirable side effect of quantization-aware training, in which quantized weights frequently jump between two quantized levels, resulting in training inst…