hadarser / SetToGraphPaper
SetToGraph paper repository
☆21Updated 4 years ago
Alternatives and similar repositories for SetToGraphPaper:
Users that are interested in SetToGraphPaper are comparing it to the libraries listed below
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆35Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- Equivalence Between Structural Representations and Positional Node Embeddings☆21Updated 4 years ago
- ☆45Updated 4 years ago
- Code for Graph Normalizing Flows.☆62Updated 5 years ago
- Code for the ICLR 2019 paper "Invariant and Equiovariant Graph Networks"☆24Updated 4 years ago
- Codes for NIPS 2019 Paper: Rethinking Kernel Methods for Node Representation Learning on Graphs☆34Updated 4 years ago
- ☆12Updated 3 years ago
- Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.☆20Updated last year
- Pytorch implementation for "Particle Flow Bayes' Rule"☆14Updated 5 years ago
- ☆31Updated 4 years ago
- ☆30Updated last year
- ☆26Updated 6 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆54Updated 3 years ago
- Variational Graph Convolutional Networks☆22Updated 4 years ago
- ☆26Updated 3 years ago
- ☆20Updated 3 years ago
- Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling☆110Updated last year
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆57Updated 4 months ago
- ☆35Updated 5 years ago
- Mixed-curvature Variational Autoencoders (ICLR 2020)☆60Updated 4 years ago
- ☆17Updated last year
- Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)☆23Updated last year
- ICML 2020 Paper: Latent Variable Modelling with Hyperbolic Normalizing Flows☆53Updated 2 years ago
- [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling☆42Updated last year
- ☆18Updated 3 years ago
- Code for Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.☆30Updated 4 years ago
- ☆16Updated 3 years ago
- Hyperbolic Neural Networks, pytorch☆86Updated 5 years ago
- Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper☆50Updated 3 years ago