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20182026
most citedImproving Neural Network Quantization without Retraining using Outlier Channel Splitting

151 citations · 308 across the 15 of their papers we have counts for

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8 papers · 1 filter

cs.LG2025

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization

Jiaqi Tang, Yu Xia, Yi-Feng Wu +9

The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominanc…

cs.LG2024

DiffKV: Differentiated Memory Management for Large Language Models with Parallel KV Compaction

Yanqi Zhang, Yuwei Hu, Runyuan Zhao +2

Large language models (LLMs) demonstrate remarkable capabilities but face substantial serving costs due to their high memory demands, with the key-value (KV) cache being a primary…

cs.LG2024

Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning

Ziyu Zhao, Leilei Gan, Guoyin Wang +5

Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs). Its modular and plug-and-play nature allows the integration of various domain-specific…

cs.LG2024

Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level

Runlin Lei, Yuwei Hu, Yuchen Ren +1

Graph Neural Networks (GNNs) excel across various applications but remain vulnerable to adversarial attacks, particularly Graph Injection Attacks (GIAs), which inject malicious nod…

cs.LG2020★ 12 cited

FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

Yuwei Hu, Zihao Ye, Minjie Wang +6

Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…

cs.LG2019★ 151 cited

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2

Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature foc…

cs.LG2018★ 5 cited

Building Efficient Deep Neural Networks with Unitary Group Convolutions

Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2

We propose unitary group convolutions (UGConvs), a building block for CNNs which compose a group convolution with unitary transforms in feature space to learn a richer set of repre…

cs.LG2018★ 135 cited

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang +9

There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narr…