3 citations · 7 across the 6 of their papers we have counts for
6 papers
Efficient LLM Scheduling by Learning to Rank
Yichao Fu, Siqi Zhu, Runlong Su +3
In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple Fir…
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
Yichao Fu, Peter Bailis, Ion Stoica +1
Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded, resulting in high latency and significant wastes of the parallel processing power of modern acc…
Unified Matrix Factorization with Dynamic Multi-view Clustering
Shangde Gao, Ke Liu, Yichao Fu
Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional…
Contrastive Knowledge Amalgamation for Unsupervised Image Classification
Shangde Gao, Yichao Fu, Ke Liu +1
Knowledge amalgamation (KA) aims to learn a compact student model to handle the joint objective from multiple teacher models that are are specialized for their own tasks respective…
Feature Map Testing for Deep Neural Networks
Dong Huang, Qingwen Bu, Yahao Qing +2
Due to the widespread application of deep neural networks~(DNNs) in safety-critical tasks, deep learning testing has drawn increasing attention. During the testing process, test ca…
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing
Dong Huang, Qingwen Bu, Yichao Fu +3
Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect…