smeznar / HVAELinks
An approach for embedding hierarhical structures into a continuous vector space using variational autoencoders.
☆26Updated last year
Alternatives and similar repositories for HVAE
Users that are interested in HVAE are comparing it to the libraries listed below
Sorting:
- Official implementation of E(n)-equivariant Graph Neural Cellular Automata☆29Updated last year
- [DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery☆33Updated last year
- Dynamic causal Bayesian optimisation☆38Updated 2 years ago
- Fine-grained, dynamic control of neural network topology in JAX.☆21Updated last year
- Differentiable Euler Characteristic Transform☆17Updated last year
- ☆16Updated 4 years ago
- A paper describing the implementation of PySR and SymbolicRegression.jl☆58Updated last year
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆87Updated 3 years ago
- Deep Graph Mapper: Seeing Graphs through the Neural Lens☆58Updated last year
- Material for the hands-on tutorial on Graph Deep Learning held at the Alan Turing Institute☆58Updated last year
- Codes for the Numerical Results of the paper "Tangent Bundle Neural Networks: from Manifolds to Celullar Sheaves and Back"☆11Updated 2 years ago
- Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inf…☆24Updated 8 months ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆18Updated 9 months ago
- AI Hilbert is an algebraic geometric based discovery system (based on Putinar's Positivstellensatz), that enables the discovery of fundam…☆32Updated 11 months ago
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆27Updated 5 years ago
- Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".☆53Updated 4 months ago
- symbolic regression☆39Updated 2 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling☆29Updated last year
- In which I learn about score functions and how they can be used to generate data.☆16Updated last year
- Code related to different aspects of conformal learning☆16Updated 5 months ago
- Code for verifying deep neural feature ansatz☆19Updated 2 years ago
- Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…☆22Updated 2 weeks ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆29Updated 4 months ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆42Updated 10 months ago
- ☆21Updated 2 years ago
- A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes☆25Updated 3 years ago
- Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.☆50Updated 2 years ago
- Implementation of GPLVM and Bayesian GPLVM in pytorch/gpytorch☆15Updated 4 years ago
- Representation Learning on Topological Domains☆81Updated this week