rish-16 / nam-pytorchLinks
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.
☆14Updated 4 years ago
Alternatives and similar repositories for nam-pytorch
Users that are interested in nam-pytorch are comparing it to the libraries listed below
Sorting:
- Self-Supervised Noise Embeddings (Self-SNE)☆158Updated 10 months ago
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆29Updated 5 years ago
- Pytorch implementation of VAEs for heterogeneous likelihoods.☆43Updated 3 years ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆22Updated last year
- Contrastive neighbor embeddings☆57Updated 3 months ago
- A Python package for intrinsic dimension estimation☆96Updated 4 months ago
- ☆51Updated last year
- Contains public materials for students enrolled in MITx: 6.871x, Machine Learning for Healthcare☆20Updated 4 years ago
- AutoML Two-Sample Test☆19Updated 3 years ago
- Hopefully, a compact and general-purpose Python package for Multiperturbation Shapley value Analysis (MSA).☆19Updated 6 months ago
- Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep…☆152Updated 5 years ago
- Explaining dimensionality results using SHAP values☆55Updated last month
- Code accompanying paper "Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets."☆29Updated 3 years ago
- Repo for open sourcing the NAMs.☆25Updated 5 years ago
- Implementations of growing and pruning in neural networks☆22Updated 2 years ago
- A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.☆22Updated 6 years ago
- ☆24Updated 4 years ago
- Official codebase for "Distribution-Free, Risk-Controlling Prediction Sets"☆88Updated 2 years ago
- Conceptual & empirical comparisons between decision forests & deep networks☆18Updated 8 months ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆20Updated 3 years ago
- Composable kernels for scikit-learn implemented in JAX.☆47Updated 5 years ago
- Material for STATS271: Applied Bayesian Statistics (Spring 2021)☆28Updated 4 years ago
- ☆22Updated 11 months ago
- #UAI2020 Codes for PAC-Bayesian Contrastive Unsupervised Representation Learning☆14Updated 3 years ago
- ☆18Updated 6 years ago
- Hyperbolic PCA via Horospherical Projections☆75Updated 2 years ago
- Training and evaluating NBM and SPAM for interpretable machine learning.☆78Updated 2 years ago
- Neural Additive Models (Google Research)☆74Updated 4 years ago
- The Union of Intersections Framework in Python☆15Updated last month
- Random feature latent variable models in Python☆23Updated 2 years ago