most citedHow Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

36 citations · 49 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NE20231 cited

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration

Shaoyi Huang, Haowen Fang, Kaleel Mahmood +7

Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event…

cs.CV20231 cited

PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis

Shu Wei, Nuo Xu

Document layout analysis has a wide range of requirements across various domains, languages, and business scenarios. However, most current state-of-the-art algorithms are language-…

cs.CL202336 cited

How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

Xuanting Chen, Junjie Ye, Can Zu +7

The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. Ho…

cs.CR20235 cited

RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference

Hongwu Peng, Shanglin Zhou, Yukui Luo +11

The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…

cs.CR20226 cited

CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference

Ran Ran, Nuo Xu, Wei Wang +3

Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal healthcare and financial…