Search Results for author:
Found 20 papers, 10 papers with code
AutoG: Towards automatic graph construction from tabular data
The absence of dedicated datasets to formalize and evaluate the effectiveness of graph construction methods, and 2.
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains.
Improving Causal Reasoning in Large Language Models: A Survey
In this survey, we provide a comprehensive review of research aimed at enhancing LLMs for causal reasoning.
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
To address this gap, our work aims to explore the potential of LLMs in the context of adversarial attacks on graphs.
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP.
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs.
Graph Machine Learning in the Era of Large Language Models (LLMs)
Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
Technically, SATeCo freezes all the parameters of the pre-trained UNet and VAE, and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules, in the decoder of UNet and VAE.
Ranked #9 on
Video Super-Resolution
on Vid4 - 4x upscaling
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Towards Neural Scaling Laws on Graphs
Yet, the neural scaling laws on graphs, i. e., how the performance of deep graph models changes with model and dataset sizes, have not been systematically investigated, casting doubts on the feasibility of achieving large graph models.
Position: Graph Foundation Models are Already Here
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
Inter-frame Accelerate Attack against Video Interpolation Models
We apply adversarial attacks to VIF models and find that the VIF models are very vulnerable to adversarial examples.
Enhancing the Transferability of Adversarial Attacks via Scale Ensemble
The experiments on ImageNet show that our method successfully mitigates the gap of transferability between models with different input sizes and achieves about 8% higher success rate comparing with the state-of-the-art input transformation methods.
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes
This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks.
Appending Adversarial Frames for Universal Video Attack
There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied.
Unsupervised Single Image Deraining with Self-supervised Constraints
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications.
