Search Results for author:
Found 27 papers, 11 papers with code
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Self-supervised Learning on Graphs: Deep Insights and New Direction
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
Node Similarity Preserving Graph Convolutional Networks
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
Elastic Graph Neural Networks
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
Gophormer: Ego-Graph Transformer for Node Classification
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
Graph Neural Networks for Multimodal Single-Cell Data Integration
Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
A Comprehensive Survey on Trustworthy Recommender Systems
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.
Test-Time Training for Graph Neural Networks
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
Multi-Prompt with Depth Partitioned Cross-Modal Learning
In recent years, soft prompt learning methods have been proposed to fine-tune large-scale vision-language pre-trained models for various downstream tasks.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
To mitigate this issue, we manually curate a benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks.
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline
Based on OpenStereo, we conducted experiments and have achieved or surpassed the performance metrics reported in the original paper.
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
In this survey, we comprehensively review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of MLLMs, introduce recent trends in applications of MLLMs on reasoning-intensive tasks, and finally discuss current practices and future directions.
COCO is "ALL'' You Need for Visual Instruction Fine-tuning
We argue that datasets with diverse and high-quality detailed instruction following annotations are essential and adequate for MLLMs IFT.
Ranked #43 on
Visual Question Answering
on MM-Vet
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently.
Ranked #37 on
Visual Question Answering
on MM-Vet
