Search Results for author:
Found 39 papers, 5 papers with code
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE) model, with the goal of enhancing performance in each domain while retaining effectiveness on general tasks.
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
Future research needs to continue to explore how to efficiently fuse LLMs and GNNs to achieve more powerful graph learning and reasoning capabilities and provide new impetus for the development of graph mining techniques.
GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization
In addition, to further address the class imbalance problem, we design a dynamic group-aware margin strategy based on reinforcement learning to provide appropriate and unbiased margins for different groups.
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation
In particular, we introduce the concept of an event skeleton for core representation semantics and simplify the typically complex data augmentation techniques found in existing graph contrastive learning to boost algorithmic efficiency.
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation
Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e. g., audio, images, text, video, etc.).
Emotion Recognition in Conversation
Multimodal Emotion Recognition
SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition
To address these issues, we propose a Spectral Domain Reconstruction Graph Neural Network (SDR-GNN) for incomplete multimodal learning in conversational emotion recognition.
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification
To address these limitations, we propose a novel method of contrastive multi-graph learning with neighbor hierarchical sifting for semi-supervised text classification, namely ConNHS.
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics.
MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment
Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems.
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification
Additionally, we propose a self-correction mechanism to mitigate the loss of true negative samples caused by clustering inconsistency.
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions.
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation
Unlike traditional unimodal emotion recognition, MERC can fuse complementary semantic information between multiple modalities (e. g., text, audio, and vision) to improve emotion recognition.
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation
In the feature extraction stage, we introduce a graph structure to construct face images as input and then design a Multi-view Mask Contrastive Learning (MMCL) mechanism to learn complex structural and semantic information about face images.
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
Existing methods focus on using graph neural networks (GNN) to model conversational relationships and capture contextual latent semantic relationships.
Ranked #1 on
Emotion Recognition in Conversation
on MELD
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
Large language models (LLMs) have significantly advanced various natural language processing tasks, but deploying them remains computationally expensive.
Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios
The first signal is the student's self-consistency (consistency of student multiple outputs), which is a proxy of the student's confidence.
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Since consistency and complementarity information correspond to low-frequency and high-frequency information, respectively, this paper revisits the problem of multimodal emotion recognition in conversation from the perspective of the graph spectrum.
Ranked #2 on
Emotion Recognition in Conversation
on IEMOCAP
Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses
While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas.
The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education
Assessing instruction quality is a fundamental component of any improvement efforts in the education system.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
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.
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
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.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
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
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.
Fast Butterfly-Core Community Search For Large Labeled Graphs
Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph.
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
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.
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition
Specifically, we construct a weighted multi-relationship graph to simultaneously capture the dependencies between speakers and event relations in a dialogue.
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations
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.
A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning
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.
Graph Information Bottleneck for Remote Sensing Segmentation
Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information.
CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation
However, existing CLIP-based age estimation methods require high memory usage (quadratic complexity) when globally modeling images, and lack an error feedback mechanism to prompt the model about the quality of age prediction results.
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults
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
In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
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.
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
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.
Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
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.
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
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.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
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.
