80 citations · 118 across the 5 of their papers we have counts for
5 papers
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
Jun Zhang, Jue Wang, Huan Li +4
We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Models (LLMs) without the need for an auxiliary model. This approach is characterize…
LINDT: Tackling Negative Federated Learning with Local Adaptation
Hong Lin, Lidan Shou, Ke Chen +2
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without dis…
Feature Importance Ranking for Deep Learning
Maksymilian Wojtas, Ke Chen
Feature importance ranking has become a powerful tool for explainable AI. However, its nature of combinatorial optimization poses a great challenge for deep learning. In this paper…
A Framework for End-to-End Learning on Semantic Tree-Structured Data
William Woof, Ke Chen
While learning models are typically studied for inputs in the form of a fixed dimensional feature vector, real world data is rarely found in this form. In order to meet the basic r…
Towards Further Understanding of Sparse Filtering via Information Bottleneck
Fabio Massimo Zennaro, Ke Chen
In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in t…