2 citations · 2 across the 9 of their papers we have counts for
3 papers · 1 filter
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
Qinsi Wang, Saeed Vahidian, Hancheng Ye +3
Large language models (LLMs) with billions of parameters have sparked a new wave of exciting AI applications. However, their high computational costs and memory demands during infe…
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu +4
Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achi…
Exploring the Impact of Dataset Bias on Dataset Distillation
Yao Lu, Jianyang Gu, Xuguang Chen +2
Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that preserves essential information from the original dataset. This synthetic dataset can serve…