chenweize1998 / fully-hyperbolic-nn
Code for paper Fully Hyperbolic Neural Networks
☆77Updated 2 years ago
Alternatives and similar repositories for fully-hyperbolic-nn:
Users that are interested in fully-hyperbolic-nn are comparing it to the libraries listed below
- Official PyTorch implementation of Hyperbolic Neural Networks++☆66Updated 3 years ago
- Hyperbolic Neural Networks, pytorch☆86Updated 5 years ago
- Papers and Codes for the deep learning in hyperbolic space☆172Updated 2 years ago
- Code for ICML 2020 "Graph Optimal Transport for Cross-Domain Alignment"☆153Updated 4 years ago
- The repository for Hyperbolic Representation Learning for Computer Vision, ECCV 2022☆62Updated 2 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- ☆29Updated 2 years ago
- Code for the paper "A Fully Hyperbolic Neural Model for Hierarchical Multi-class Classification"☆16Updated 4 years ago
- Code to reproduce the results for Compositional Attention☆60Updated 2 years ago
- Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Z…☆44Updated 4 years ago
- "Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')☆47Updated 2 years ago
- This is the official repository for the paper "Laplacian Features for Learning with Hyperbolic Space"☆13Updated 2 years ago
- Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.☆54Updated last year
- Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022☆250Updated 3 years ago
- Mixed-curvature Variational Autoencoders (ICLR 2020)☆60Updated 4 years ago
- Facilitating learning, using, and designing graph processing pipelines/models systematically.☆27Updated 2 years ago
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆45Updated 4 years ago
- An extension of the PyTorch library containing various tools for performing deep learning in hyperbolic space.☆151Updated last month
- PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))☆17Updated 7 months ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆54Updated 3 years ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆25Updated 2 years ago
- List of Publications in Graph Contrastive Learning☆35Updated 2 years ago
- Implicit Graph Neural Networks☆60Updated 3 years ago
- ☆21Updated 3 years ago
- Implementation of Multi-View Information Bottleneck☆129Updated 4 years ago
- ☆26Updated 2 years ago
- Diffusion Models for Graphs Benefit From Discrete State Spaces☆31Updated last year
- Official source code for "Graph Neural Networks for Learning Equivariant Representations of Neural Networks". In ICLR 2024 (oral).☆78Updated 6 months ago
- PyG re-implementation of Neural Bellman-Ford Networks (NeurIPS 2021)☆65Updated 2 years ago
- The code for the ICML 2021 paper "Graph Neural Networks Inspired by Classical Iterative Algorithms".☆43Updated 3 years ago