Search Results for author: YuHang Zhou

Found 22 papers, 5 papers with code

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

no code implementations14 Mar 2024 Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, YuHang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.

Causal Inference Fairness

From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter

no code implementations22 Feb 2024 YuHang Zhou, Xuan Lu, Wei Ai

In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution.

Sentiment Analysis Sentiment Classification

Are Large Language Models Rational Investors?

no code implementations20 Feb 2024 YuHang Zhou, Yuchen Ni, Xiang Liu, Jian Zhang, Sen Liu, Guangnan Ye, Hongfeng Chai

Large Language Models (LLMs) are progressively being adopted in financial analysis to harness their extensive knowledge base for interpreting complex market data and trends.

Decision Making Navigate

Invariance-powered Trustworthy Defense via Remove Then Restore

no code implementations1 Feb 2024 Xiaowei Fu, YuHang Zhou, Lina Ma, Lei Zhang

Based on this finding, a Pixel Surgery and Semantic Regeneration (PSSR) model following the targeted therapy mechanism is developed, which has three merits: 1) To remove the salient attack, a score-based Pixel Surgery module is proposed, which retains the trivial attack as a kind of invariance information.

Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications

no code implementations22 Jan 2024 YuHang Zhou, Paiheng Xu, Xiyao Wang, Xuan Lu, Ge Gao, Wei Ai

Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.

Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences

1 code implementation19 Jan 2024 Xiyao Wang, YuHang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang

However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated.

Language Modelling Large Language Model

Explore Spurious Correlations at the Concept Level in Language Models for Text Classification

no code implementations15 Nov 2023 YuHang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang

We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.

counterfactual In-Context Learning +2

$R^3$-NL2GQL: A Hybrid Models Approach for for Accuracy Enhancing and Hallucinations Mitigation

1 code implementation3 Nov 2023 YuHang Zhou, He Yu, Siyu Tian, Dan Chen, Liuzhi Zhou, Xinlin Yu, Chuanjun Ji, Sen Liu, Guangnan Ye, Hongfeng Chai

While current NL2SQL tasks constructed using Foundation Models have achieved commendable results, their direct application to Natural Language to Graph Query Language (NL2GQL) tasks poses challenges due to the significant differences between GQL and SQL expressions, as well as the numerous types of GQL.

Knowledge Graphs Natural Language Queries +2

Emoji Promotes Developer Participation and Issue Resolution on GitHub

no code implementations30 Aug 2023 YuHang Zhou, Xuan Lu, Ge Gao, Qiaozhu Mei, Wei Ai

In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.

Causal Inference

Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning

1 code implementation3 Aug 2023 YuHang Zhou, Jiangchao Yao, Feng Hong, Ya zhang, Yanfeng Wang

By dynamically manipulating the gradient during training based on these factors, BDR can effectively alleviate knowledge destruction and improve knowledge reconstruction.

Class Incremental Learning Incremental Learning

Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices

no code implementations1 Jun 2023 Jing Zhu, YuHang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra

While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.

Link Prediction Node Classification

GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint

no code implementations25 May 2023 Paiheng Xu, YuHang Zhou, Bang An, Wei Ai, Furong Huang

Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention.

Fairness Link Prediction

Scalable Prompt Generation for Semi-supervised Learning with Language Models

no code implementations18 Feb 2023 YuHang Zhou, Suraj Maharjan, Beiye Liu

In this paper, we propose two methods to automatically design multiple prompts and integrate automatic verbalizer in SSL settings without sacrificing performance.

Few-Shot Learning Natural Language Understanding

Swin MAE: Masked Autoencoders for Small Datasets

1 code implementation28 Dec 2022 Zi'an Xu, Yin Dai, Fayu Liu, Weibing Chen, Yue Liu, Lifu Shi, Sheng Liu, YuHang Zhou

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets.

Transfer Learning

AutoMine: An Unmanned Mine Dataset

no code implementations CVPR 2022 Yuchen Li, Zixuan Li, Siyu Teng, Yu Zhang, YuHang Zhou, Yuchang Zhu, Dongpu Cao, Bin Tian, Yunfeng Ai, Zhe XuanYuan, Long Chen

The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.

Autonomous Driving

A General Traffic Shaping Protocol in E-Commerce

no code implementations30 Dec 2021 Chenlin Shen, Guangda Huzhang, YuHang Zhou, Chen Liang, Qing Da

Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.

MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets

no code implementations5 Aug 2021 Shixiang Feng, YuHang Zhou, Xiaoman Zhang, Ya zhang, Yanfeng Wang

A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network.

Knowledge Distillation Organ Segmentation +1

On the Robustness of Domain Adaption to Adversarial Attacks

no code implementations4 Aug 2021 Liyuan Zhang, YuHang Zhou, Lei Zhang

State-of-the-art deep neural networks (DNNs) have been proved to have excellent performance on unsupervised domain adaption (UDA).

Adversarial Attack Pseudo Label +1

Uncertainty-aware Incremental Learning for Multi-organ Segmentation

no code implementations9 Mar 2021 YuHang Zhou, Xiaoman Zhang, Shixiang Feng, Ya zhang, Yanfeng

Specifically, given a pretrained $K$ organ segmentation model and a new single-organ dataset, we train a unified $K+1$ organ segmentation model without accessing any data belonging to the previous training stages.

Ethics Incremental Learning +3

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation

no code implementations13 Oct 2020 Xiaoman Zhang, Shixiang Feng, YuHang Zhou, Ya zhang, Yanfeng Wang

We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation.

Brain Tumor Segmentation Segmentation +3

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