FilippoMB / Total-variation-graph-neural-networksLinks
Pytorch (PyG) and Tensorflow (Keras/Spektral) implementation of Total Variation Graph Neural Network (TVGNN), as presented at ICML 2023.
☆20Updated 10 months ago
Alternatives and similar repositories for Total-variation-graph-neural-networks
Users that are interested in Total-variation-graph-neural-networks are comparing it to the libraries listed below
Sorting:
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated 2 years ago
- ☆13Updated 4 years ago
- Official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21)☆10Updated 3 years ago
- Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning☆20Updated 4 years ago
- ☆11Updated 3 years ago
- PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularl…☆25Updated 2 years ago
- Official code repository for the papers "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" accepted at ICLR 2023; "Non-D…☆15Updated last year
- ☆21Updated 4 years ago
- Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"☆39Updated 3 years ago
- Official repository for Cell Attention Networks☆14Updated 2 years ago
- SetToGraph paper repository☆22Updated 5 years ago
- Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)☆23Updated 2 years ago
- Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561☆25Updated 4 years ago
- ☆39Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…☆107Updated 4 years ago
- Code for "Theoretical Foundations of Deep Selective State-Space Models" (NeurIPS 2024)☆15Updated last year
- A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).☆21Updated last year
- This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)☆12Updated 3 years ago
- [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…☆14Updated 3 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆37Updated 5 years ago
- Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called si…☆82Updated 4 years ago
- [ICML 2024] Recurrent Distance Filtering for Graph Representation Learning☆15Updated last year
- Repo for the paper "Landscape Surrogate Learning Decision Losses for Mathematical Optimization Under Partial Information"☆38Updated 2 years ago
- ☆28Updated 2 years ago
- ☆13Updated 4 years ago
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆61Updated last year
- Euclidean Wasserstein-2 optimal transportation☆47Updated 2 years ago
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆32Updated 3 years ago
- Quantification of Uncertainty with Adversarial Models☆29Updated 2 years ago