aradha / deep_neural_feature_ansatzLinks
Code for verifying deep neural feature ansatz
☆19Updated 2 years ago
Alternatives and similar repositories for deep_neural_feature_ansatz
Users that are interested in deep_neural_feature_ansatz are comparing it to the libraries listed below
Sorting:
- ☆56Updated 4 months ago
- Scalable and Stable Parallelization of Nonlinear RNNS☆20Updated this week
- ☆26Updated 2 years ago
- Code for experiments on transformers using Markovian data.☆19Updated 9 months ago
- Mutual information estimators and benchmark☆51Updated 7 months ago
- Hierarchical Associative Memory User Experience☆103Updated last month
- Omnigrok: Grokking Beyond Algorithmic Data☆61Updated 2 years ago
- ☆23Updated last year
- The Energy Transformer block, in JAX☆59Updated last year
- ☆70Updated 8 months ago
- Agustinus' very opiniated publication-ready plotting library☆69Updated 3 months ago
- ☆14Updated 9 months ago
- Predictive Coding JAX-based library☆78Updated 4 months ago
- Parallelizing non-linear sequential models over the sequence length☆54Updated 2 months ago
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆89Updated 3 years ago
- ☆14Updated last year
- ☆63Updated 5 months ago
- This repository contains the official code for Energy Transformer---an efficient Energy-based Transformer variant for graph classificatio…☆25Updated last year
- Scalable training and inference for Probabilistic Circuits☆71Updated last month
- ☆21Updated 4 months ago
- ☆14Updated 4 years ago
- Deep Networks Grok All the Time and Here is Why☆37Updated last year
- Official PyTorch implementation of NeuralSVD (ICML 2024)☆20Updated 11 months ago
- ☆56Updated 10 months ago
- Non official implementation of the Linear Recurrent Unit (LRU, Orvieto et al. 2023)☆56Updated last month
- ☆25Updated last year
- ☆15Updated last year
- ☆27Updated 2 years ago
- Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024☆28Updated 11 months ago
- Training and evaluating NBM and SPAM for interpretable machine learning.☆78Updated 2 years ago