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Found 24 papers, 11 papers with code
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
In this work, we introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective state space models for STG learning by treating STG Network as a system, and employing the Graph Selective State Space Block (GS3B) to precisely characterize the dynamic evolution of STG networks.
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Deep generative models (DGMs) have been widely developed for graph data.
A Physics-guided Generative AI Toolkit for Geophysical Monitoring
Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion
This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem.
Denoising Variational Graph of Graphs Auto-Encoder for Predicting Structured Entity Interactions
The interactions between structured entities play important roles in a wide range of applications such as chemistry, material science, biology, and medical science.
$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data.
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator
Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice.
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Graph Neural Networks (GNNs) are effective tools for graph representation learning.
Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph
This degree difference between equivalent entities poses a great challenge for entity alignment.
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.
Iterative Teaching by Label Synthesis
In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.
Pre-training Molecular Graph Representation with 3D Geometry
However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation.
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
This is accomplished by applying two operations on the input data to the NN model: 1) The crosscorrelation of the input data (i. e., shot gather, seismic image, etc.)
Matching Point Sets with Quantum Circuit Learning
In this work, we propose a parameterised quantum circuit learning approach to point set matching problem.
Unsupervised Point Cloud Pre-Training via Occlusion Completion
We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.
3D Point Cloud Linear Classification
Few-Shot 3D Point Cloud Classification
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Pre-Training by Completing Point Clouds
There has recently been a flurry of exciting advances in deep learning models on point clouds.
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.
Binarized Graph Neural Network
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
Neural Random Subspace
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions.
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.
