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- Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks167
This paper proposes Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection and introduces multi-channel graph neural networks to learn multimodal representations based on the global characteristics of the dataset.
- COSTA124
COSTA, a novel Covariance-preServing feaTure space Augmentation framework for GCL is proposed, which generates augmented features by maintaining a "good sketch" of original features, and it is shown that the feature augmentation with COSTA achieves comparable/better results than graph augmentation based models.
- A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges94
This work charts out a comprehensive road-map for Trustworthy Federated Learning (TFL) and provides an overview of existing efforts across four pivotal dimensions: Privacy and Security, Robustness, Fairness, and Explainability.
- A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy90
This survey outlines the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment, and discusses specific technical solutions for realizing each aspect of Trustworthy FL (TFL).
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- #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate71
It is found that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness), which allows for a nuanced analysis of the interplay between botness, partisanship and influence.
- Low-Rank Adaptation for Foundation Models: A Comprehensive Review67
This survey provides the first comprehensive review of LoRA techniques beyond large Language Models to general foundation models, including recent techniques foundations, emerging frontiers and applications of low-rank adaptation across multiple domains.
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- Recent Advances of Multimodal Continual Learning: A Comprehensive Survey56
The first comprehensive survey on MMCL is presented, providing essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods, which categorize MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods.
- Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt45
This work proposes a novel Generative Multimodal Prompt (GMP) model for MABSA, which includes the Multinodal Encoder module and the N-Stream Decoders module, and introduces a subtask to predict the number of aspect terms in each instance to construct the multimodal prompt.
- Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification45
A probabilistic model is proposed to infer the edge weights in this graph, which can be jointly trained with the subsequent node classification component and the effectiveness of the method and the rationality of the optimal graph is demonstrated.
- MM-InstructEval: Zero-Shot Evaluation of (Multimodal) Large Language Models on Multimodal Reasoning Tasks35
This paper conducts extensive zero-shot evaluations on 45 models (including 36 MLLMs) across 16 multimodal datasets, encompassing 6 distinct tasks using 10 different instructions and introduces multiple innovative metrics, including the 'Best Performance' metric to benchmark peak model capabilities.
- Graph-adaptive Rectified Linear Unit for Graph Neural Networks35
This work proposes Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric activation function incorporating the neighborhood information in a novel and efficient way and conducts comprehensive experiments to show that the plug-and-play GReLU method is efficient and effective given different GNN backbones and various downstream tasks.
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- Contrastive Cross-scale Graph Knowledge Synergy33
To promote cross-scale information sharing and knowledge interactions among GP, a novel joint optimization formula that contains a pairwise contrastive loss between any two coarse-grained graph views is proposed that promotes knowledge sharing that yields informative representations, but also stabilizes the training process.
- Semi-supervised Multi-label Learning for Graph-structured Data33
This work incorporates the idea of label embedding into the proposed model to capture both network topology and higher-order multi-label correlations and demonstrates that the model significantly outperforms the state-of-the-art models for node-level tasks.
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- Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space32
This work proposes an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: adaptive graph convolutional hashing, latent feature dispersion, and Fourier serialized gradient estimation.
- oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation30
OneDNN Graph Compiler is presented, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high-performance code generation of the deep neural network graph.
- AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation29
AnnaAgent is proposed, an emotional and cognitive dynamic agent system equipped with tertiary memory that achieves more realistic seeker simulation in psychological counseling compared to existing baselines.
- Dual-view hypergraph neural networks for attributed graph learning28
A Dual-view HyperGraph Neural Network (DHGNN) model for attributed graph learning that unify the expression form of different information sources of nodes by hypergraph, and construct dual hypergraphs according to topology and attributes of nodes.
- Doc2hash: Learning Discrete Latent variables for Documents Retrieval25
This paper proposes a method, Doc2hash, that solves the gradient flow problem of the discrete stochastic layer by using continuous relaxation on priors, and trains the generative model in an end-to-end manner to generate hash codes.
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- Geometric View of Soft Decorrelation in Self-Supervised Learning22
This work provides a new perspective on the geometric distance between positive definite matrices to investigate why the soft decorrelation cannot efficiently solve the dimensional collapse and constructs a family of loss functions utilizing the Bregman Matrix Divergence.
- WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering21
This work proposes WSFE, a model-agnostic and training-free representation encoder, to be flexibly employed on the fly for effective user segmentation and demonstrates the superiority and generality of WSFE to fuel multiple downstream tasks with diverse underlying targets in recommendation.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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