Alternatives and similar repositories for recur

Users that are interested in recur are comparing it to the libraries listed below

• facebookresearch / symbolicregression
  SR based on LLMs.
  ☆127Updated 3 years ago
• omron-sinicx / srsd-benchmark
  [DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
  ☆38Updated last year
• IDSIA / neuraldiffeq-fwp
  Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (…
  ☆23Updated 8 months ago
• deep-symbolic-mathematics / Multimodal-Math-Pretraining
  [ICLR 2024 Spotlight] This is the official code for the paper "SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-t…
  ☆57Updated last year
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆122Updated 4 years ago
• tristandeleu / jax-dag-gflownet
  Code for "Bayesian Structure Learning with Generative Flow Networks"
  ☆95Updated 3 years ago
• mojivalipour / symbolicgpt
  Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. In…
  ☆56Updated 4 years ago
• EleMisi / VAEL
  Codebase for VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
  ☆23Updated 2 years ago
• EdinburghNLP / torch-adaptive-imle
  ☆35Updated last year
• pietrobarbiero / logic_explained_networks
  Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.
  ☆53Updated 2 years ago
• pylon-lib / pylon
  ☆123Updated 2 years ago
• ermongroup / SPN_Variational_Inference
  PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
  ☆17Updated 4 years ago
• neildhir / DCBO
  Dynamic causal Bayesian optimisation
  ☆40Updated 2 years ago
• crunchiness / lernd
  Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
  ☆27Updated 2 years ago
• dylandoblar / noether-networks
  Meta-learning inductive biases in the form of useful conserved quantities.
  ☆39Updated 3 years ago
• microsoft / goodpoints
  A Python package for generating concise, high-quality summaries of a probability distribution
  ☆57Updated 3 weeks ago
• google-research / hyperbo
  Pre-trained Gaussian processes for Bayesian optimization
  ☆100Updated last month
• google-deepmind / neural_testbed
  ☆195Updated last month
• bhoov / energy-transformer-jax
  The Energy Transformer block, in JAX
  ☆63Updated 2 years ago
• HEmile / a-nesi
  A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
  ☆23Updated last year
• cavalab / srbench
  A living benchmark framework for symbolic regression
  ☆292Updated 3 months ago
• bhoov / hamux
  Hierarchical Associative Memory User Experience
  ☆106Updated 3 weeks ago
• FutureComputing4AI / Hrrformer
  Hrrformer: A Neuro-symbolic Self-attention Model (ICML23)
  ☆61Updated 4 months ago
• nalexai / hyperlib
  Library that contains implementations of machine learning components in the hyperbolic space
  ☆145Updated last year
• deep-symbolic-mathematics / TPSR
  [NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
  ☆79Updated last year
• pilancilab / scnn
  Scalable Convex Neural Networks
  ☆25Updated 9 months ago
• vvvm23 / mamba-jax
  Unofficial but Efficient Implementation of "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" in JAX
  ☆93Updated 2 years ago
• MahmudulAlam / Holographic-Reduced-Representations
  Holographic Reduced Representations
  ☆30Updated 2 months ago
• smeznar / HVAE
  An approach for embedding hierarhical structures into a continuous vector space using variational autoencoders.
  ☆29Updated 2 months ago
• Harshs27 / neural-graphical-models
  Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
  ☆30Updated 6 months ago