layer6ai-labs / UoMHLinks
☆12Updated 2 years ago
Alternatives and similar repositories for UoMH
Users that are interested in UoMH are comparing it to the libraries listed below
Sorting:
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆18Updated last year
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆35Updated 4 years ago
- ☆17Updated 6 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- ☆32Updated 7 years ago
- Source code for the "Computationally Tractable Riemannian Manifolds for Graph Embeddings" paper☆36Updated 5 years ago
- Updated code base for GlanceNets: Interpretable, Leak-proof Concept-based models☆25Updated 2 years ago
- Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.☆51Updated 2 years ago
- This is the code for the paper Embrace the Gap: VAEs perform Independent Mechanism Analysis, showing that optimizing the ELBO is equivale…☆23Updated last year
- Graph matching and clustering by comparing heat kernels via optimal transport.☆27Updated 2 years ago
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆45Updated 2 years ago
- Ultrahyperbolic Representation Learning☆13Updated 5 years ago
- Morgan A. Schmitz., Matthieu Heitz, Nicolas Bonneel, Fred Ngole, David Coeurjolly, Marco Cuturi, Gabriel Peyré, and Jean-Luc Starck. "Was…☆20Updated 5 years ago
- Python implementation of smooth optimal transport.☆60Updated 4 years ago
- Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models☆11Updated 11 months ago
- Robust Learning with the Hilbert-Schmidt Independence Criterion☆47Updated 5 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆26Updated 3 years ago
- Bayesian model reduction for probabilistic machine learning☆11Updated 2 months ago
- Random feature latent variable models in Python☆23Updated 2 years ago
- Matlab code for tree-Wasserstein distance in the paper "Tree-Sliced Variants of Wasserstein Distances", NeurIPS, 2019. (Tam Le, Makoto Y…☆11Updated 5 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆37Updated 2 years ago
- ☆17Updated 2 years ago
- Code for Sliced Gromov-Wasserstein☆69Updated 5 years ago
- PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020☆17Updated 3 years ago
- This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)☆12Updated 3 years ago
- A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).☆21Updated last year
- Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness☆47Updated 4 years ago