Search Results for author: Wei Ai

Found 20 papers, 0 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

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.

An Effective Index for Truss-based Community Search on Large Directed Graphs

no code implementations19 Jan 2024 Wei Ai, CanHao Xie, Tao Meng, Yinghao Wu, Keqin Li

Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks.

Community Detection Community Search +2

Fast Butterfly-Core Community Search For Large Labeled Graphs

no code implementations19 Jan 2024 JiaYi Du, Yinghao Wu, Wei Ai, Tao Meng, CanHao Xie, Keqin Li

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph.

Community Search

A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning

no code implementations3 Jan 2024 Wei Ai, FuChen Zhang, Tao Meng, Yuntao Shou, HongEn Shao, Keqin Li

To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).

Classification Contrastive Learning +2

Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition

no code implementations28 Dec 2023 Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

However, the existing feature fusion methods have usually mapped the features of different modalities into the same feature space for information fusion, which can not eliminate the heterogeneity between different modalities.

Contrastive Learning Graph Representation Learning +1

DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition

no code implementations17 Dec 2023 Wei Ai, Yuntao Shou, Tao Meng, Keqin Li

Specifically, we construct a weighted multi-relationship graph to simultaneously capture the dependencies between speakers and event relations in a dialogue.

Contrastive Learning Multimodal Emotion Recognition +1

Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations

no code implementations11 Dec 2023 Tao Meng, Yuntao Shou, Wei Ai, Nan Yin, Keqin Li

The main task of Multimodal Emotion Recognition in Conversations (MERC) is to identify the emotions in modalities, e. g., text, audio, image and video, which is a significant development direction for realizing machine intelligence.

Data Augmentation Generative Adversarial Network +2

A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning

no code implementations10 Dec 2023 Yuntao Shou, Tao Meng, Wei Ai, Nan Yin, Keqin Li

Unlike the traditional single-utterance multi-modal emotion recognition or single-modal conversation emotion recognition, MCER is a more challenging problem that needs to deal with more complex emotional interaction relationships.

Emotion Recognition

Graph Information Bottleneck for Remote Sensing Segmentation

no code implementations5 Dec 2023 Yuntao Shou, Wei Ai, Tao Meng

Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information.

Change Detection Contrastive Learning +3

CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation

no code implementations4 Dec 2023 Yuntao Shou, Wei Ai, Tao Meng, Keqin Li

Zero-shot age estimation aims to learn feature information about age from input images and make inferences about a given person's image or video frame without specific sample data.

Age Estimation Zero-Shot Learning

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

Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults

no code implementations12 Sep 2023 Ahmed Adel Attia, Jing Liu, Wei Ai, Dorottya Demszky, Carol Espy-Wilson

Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

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

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

Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic

no code implementations29 Jan 2023 Xuan Lu, Wei Ai, Yixin Wang, Qiaozhu Mei

While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown.

counterfactual

Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub

no code implementations10 Feb 2021 Xuan Lu, Wei Ai, Zhenpeng Chen, Yanbin Cao, Qiaozhu Mei

This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.

Management

Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy

no code implementations7 Aug 2020 Teng Ye, Wei Ai, Lingyu Zhang, Ning Luo, Lulu Zhang, Jieping Ye, Qiaozhu Mei

Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.

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