20 citations · 37 across the 9 of their papers we have counts for
9 papers
Influential Simplices Mining via Simplicial Convolutional Network
Yujie Zeng, Yiming Huang, Qiang Wu +1
Simplicial complexes have recently been in the limelight of higher-order network analysis, where a minority of simplices play crucial roles in structures and functions due to netwo…
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Zhihang Yuan, Lin Niu, Jiawei Liu +7
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be allev…
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu +6
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its e…
Wave equation for Sturm-Liouville operator with singular intermediate coefficient and potential
Michael Ruzhansky, Alibek Yeskermessuly
In this paper, we consider a wave equation on a bounded domain with a Sturm-Liouville operator with a singular intermediate coefficient and a singular potential. To obtain and eval…
Self-attention on Multi-Shifted Windows for Scene Segmentation
Litao Yu, Zhibin Li, Jian Zhang +1
Scene segmentation in images is a fundamental yet challenging problem in visual content understanding, which is to learn a model to assign every image pixel to a categorical label.…
Lightweight Vision Transformer with Cross Feature Attention
Youpeng Zhao, Huadong Tang, Yingying Jiang +2
Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to le…
Unsupervised Point Cloud Pre-Training via Contrasting and Clustering
Guofeng Mei, Xiaoshui Huang, Juan Liu +2
Annotating large-scale point clouds is highly time-consuming and often infeasible for many complex real-world tasks. Point cloud pre-training has therefore become a promising strat…
PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization
Zhihang Yuan, Chenhao Xue, Yiqi Chen +2
Quantization is one of the most effective methods to compress neural networks, which has achieved great success on convolutional neural networks (CNNs). Recently, vision transforme…
Approximation algorithms for the random-field Ising model
Tyler Helmuth, Holden Lee, Will Perkins +2
Approximating the partition function of the ferromagnetic Ising model with general external fields is known to be #BIS-hard in the worst case, even for bounded-degree graphs, and i…