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Found 24 papers, 10 papers with code
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.
LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?
Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.
Automated Bioinformatics Analysis via AutoBA
With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow.
Graph Meets LLMs: Towards Large Graph Models
In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.
FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference
The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image.
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare
Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries.
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).
Ranked #1 on
Semantic Parsing
on spider
Curriculum Graph Machine Learning: A Survey
To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery.
Unsupervised Visual Defect Detection with Score-Based Generative Model
Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs).
Use Classifier as Generator
Image recognition/classification is a widely studied problem, but its reverse problem, image generation, has drawn much less attention until recently.
Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems
Recently, neural networks based models have been widely used for recommender systems (RS).
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Out-Of-Distribution Generalization on Graphs: A Survey
This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.
Disentangled Contrastive Learning on Graphs
Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.
An Experimental Study of Quantitative Evaluations on Saliency Methods
It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first library for automated machine learning on graphs.
Noise Modulation: Let Your Model Interpret Itself
Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it, the interpretability of these models becomes an important issue. The majority of previous research works on the post-hoc interpretation of a trained model. But recently, adversarial training shows that it is possible for a model to have an interpretable input-gradient through training. However, adversarial training lacks efficiency for interpretability. To resolve this problem, we construct an approximation of the adversarial perturbations and discover a connection between adversarial training and amplitude modulation.
Quantitative Evaluations on Saliency Methods: An Experimental Study
It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics.
Efficient Reservoir Management through Deep Reinforcement Learning
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages.
Masked Gradient-Based Causal Structure Learning
This paper studies the problem of learning causal structures from observational data.
Billion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
