no code implementations • 25 Apr 2024 • Yi Han, Ge Chen, Florian Dörfler, Wenjun Mei
It turns out that the proof methods for the original discrete-time asynchronous model are no longer applicable to the analysis of the continuous-time model.
no code implementations • 15 Aug 2023 • Ziyu Zhuang, Qiguang Chen, Longxuan Ma, Mingda Li, Yi Han, Yushan Qian, Haopeng Bai, Zixian Feng, Weinan Zhang, Ting Liu
From pre-trained language model (PLM) to large language model (LLM), the field of natural language processing (NLP) has witnessed steep performance gains and wide practical uses.
no code implementations • 24 Jul 2023 • Yi Han, Matthew Chan, Eric Wengrowski, Zhuohuan Li, Nils Ole Tippenhauer, Mani Srivastava, Saman Zonouz, Luis Garcia
We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks.
no code implementations • 5 Feb 2023 • Jinyu Cai, Yi Han, Wenzhong Guo, Jicong Fan
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
no code implementations • 25 Jul 2022 • Yan Sun, Yi Han, Jicong Fan
Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data.
1 code implementation • 10 Jun 2022 • Zhichao Xu, Yi Han, Tao Yang, Anh Tran, Qingyao Ai
Seeing this gap, we propose a model named Semantic-Enhanced Bayesian Personalized Explanation Ranking (SE-BPER) to effectively combine the interaction information and semantic information.
no code implementations • 26 Sep 2021 • Yi Han, Xubin Wang, Zhengyu Lu
In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 11 Feb 2021 • Kaiwen Li, Tao Zhang, Rui Wang Yuheng Wang, Yi Han
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP).
no code implementations • 8 Jan 2021 • Nannan Wu, Qianwen Chao, Yanzhen Chen, Weiwei Xu, Chen Liu, Dinesh Manocha, Wenxin Sun, Yi Han, Xinran Yao, Xiaogang Jin
Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points.
Graphics
no code implementations • 28 Sep 2020 • Eugene Tam, Shenfei Jiang, Paul Duan, Shawn Meng, Yue Pang, Cayden Huang, Yi Han, Jacke Xie, Yuanjun Cui, Jinsong Yu, Minggui Lu
Recent advancements in deep learning have led to the widespread adoption of artificial intelligence (AI) in applications such as computer vision and natural language processing.
no code implementations • 19 Aug 2020 • Zhichao Xu, Yi Han, Yongfeng Zhang, Qingyao Ai
In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns.
2 code implementations • 7 Jul 2020 • Yi Han, Shanika Karunasekera, Christopher Leckie
(2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection.
no code implementations • 11 Feb 2020 • Yi Han, Shanika Karunasekera, Christopher Leckie
Events detected from social media streams often include early signs of accidents, crimes or disasters.
no code implementations • 25 Feb 2019 • Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
no code implementations • 17 Aug 2018 • Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.
no code implementations • 6 Apr 2017 • Yi Han, Benjamin I. P. Rubinstein
Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification.