Alternatives and similar repositories for understanding-neuralnetworks-pytorch

Users that are interested in understanding-neuralnetworks-pytorch are comparing it to the libraries listed below

• gebob19 / introduction_to_normalizing_flows
  Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'
  ☆26Updated 4 years ago
• cunningham-lab / cb_and_cc
  ☆36Updated 2 years ago
• rolandgvc / flaxvision
  A selection of neural network models ported from torchvision for JAX & Flax.
  ☆44Updated 4 years ago
• hardmaru / mdn_jax_tutorial
  Mixture Density Networks (Bishop, 1994) tutorial in JAX
  ☆59Updated 5 years ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 4 years ago
• larsmaaloee / BIVA
  BIVA: A Very Deep Hierarchy of Latent Variables forGenerative Modeling
  ☆29Updated 5 years ago
• LuisArmandoPerez / DiffusionVAE
  ☆29Updated 3 years ago
• ericjang / nf-jax
  Normalizing Flows in Jax
  ☆107Updated 4 years ago
• mblondel / teaching
  ☆85Updated 4 years ago
• PGM-Lab / probabilisticAI_tutorials
  Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2019 (https://2019.probabilistic.ai/)
  ☆20Updated 5 years ago
• shreyansh26 / ML-Optimizers-JAX
  Toy implementations of some popular ML optimizers using Python/JAX
  ☆44Updated 3 years ago
• ethanluoyc / dhmc-jax
  Discontinuous Hamiltonian Monte Carlo in JAX
  ☆41Updated 5 years ago
• SebFar / radial_bnn
  Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
  ☆33Updated 5 years ago
• didriknielsen / pixelcnn_flow
  Code for paper "Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow"
  ☆19Updated 4 years ago
• JeanKossaifi / caltech-tutorial
  Material for my Caltech tutorial on deep learning and tensor methods
  ☆70Updated 6 years ago
• ghliu / mean-field-fcdnn
  ☆28Updated 5 years ago
• DanielTakeshi / MCMC_and_Dynamics
  Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)
  ☆42Updated 5 years ago
• thegregyang / NNspectra
  Computing the eigenvalues of Neural Tangent Kernel and Conjugate Kernel (aka NNGP kernel) over the boolean cube
  ☆47Updated 5 years ago
• angusturner / generative_models
  Generative Modelling Experiments
  ☆39Updated 5 years ago
• mblondel / fenchel-young-losses
  Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
  ☆185Updated last year
• tbrx / compiled-inference
  Train neural networks to use as SMC and importance sampling proposals
  ☆24Updated 7 years ago
• franrruiz / uivi
  Code for Unbiased Implicit Variational Inference (UIVI)
  ☆14Updated 6 years ago
• kevinzakka / learn-linalg
  Learning some numerical linear algebra.
  ☆70Updated 4 years ago
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated 2 years ago
• akandykeller / SelfNormalizingFlows
  ☆68Updated 2 years ago
• j-towns / vdvae-jax
  Very deep VAEs in JAX/Flax
  ☆46Updated 3 years ago
• williamFalcon / vae_demo
  ☆9Updated 5 years ago
• henripal / sgld
  ☆59Updated 6 years ago
• dhuynh95 / fastai_bayesian
  Quick modules to turn regular Neural Networks to Bayesian Neural Networks with Dropout.
  ☆35Updated 3 years ago
• chrisorm / pydata-2018
  ☆64Updated 7 years ago