gabsens / Learning-Embeddings-into-Entropic-Wasserstein-Spaces-ENSAELinks
A thorough review of the paper "Learning Embeddings into Entropic Wasserstein Spaces" by Frogner et al. Includes a reproduction of the results on word embeddings.
☆22Updated 5 years ago
Alternatives and similar repositories for Learning-Embeddings-into-Entropic-Wasserstein-Spaces-ENSAE
Users that are interested in Learning-Embeddings-into-Entropic-Wasserstein-Spaces-ENSAE are comparing it to the libraries listed below
Sorting:
- MATLAB implementation of linear support vector classification in hyperbolic space☆20Updated 7 years ago
- Rep the Set: Neural Networks for Learning Set Representations☆29Updated 4 years ago
- ☆65Updated 10 months ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…☆62Updated 5 years ago
- Low-variance and unbiased gradient for backpropagation through categorical random variables, with application in variational auto-encoder…☆17Updated 4 years ago
- Hyperbolic Neural Networks, pytorch☆86Updated 5 years ago
- Mixed-curvature Variational Autoencoders (ICLR 2020)☆62Updated 4 years ago
- Python implementation of smooth optimal transport.☆58Updated 4 years ago
- Code accompanying our paper at AISTATS 2020☆21Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- A recreation of the Lorentz embeddings paper from ICML 2018 (Nickel 2018)☆20Updated 6 years ago
- Official implementation (learning part) for paper: Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models☆18Updated 10 months ago
- Implementation of the "Poincare Glove: Hyperbolic word embeddings" paper☆88Updated 4 years ago
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆56Updated 3 years ago
- Locally corrected Nyström (LCN), as proposed in "Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, …☆19Updated 2 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- Ancestral Gumbel-Top-k Sampling☆24Updated 5 years ago
- Gromov-Wasserstein Alignment of Embeddings☆66Updated 3 years ago
- GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices☆18Updated 3 years ago
- Code for "Rissanen Data Analysis: Examining Dataset Characteristics via Description Length" by Ethan Perez, Douwe Kiela, and Kyungyhun Ch…☆36Updated 3 years ago
- LP-SparseMAP: Differentiable sparse structured prediction in coarse factor graphs☆41Updated last year
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 4 years ago
- Source code for the paper "Hyperbolic Neural Networks", https://arxiv.org/abs/1805.09112☆178Updated 4 years ago
- implementation of different Dirichlet Variational Autoencoder Topic Models in Tensorflow☆49Updated 2 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated last year
- Code for NeurIPS 2019 paper "Hierarchical Optimal Transport for Document Representation"☆54Updated 5 years ago
- Difference-of-Entropies (DoE) Estimator☆25Updated 3 years ago
- Hyperbolic PCA via Horospherical Projections☆71Updated last year
- Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.☆56Updated last year