Alternatives and similar repositories for relative-gradient-jacobian:

Users that are interested in relative-gradient-jacobian are comparing it to the libraries listed below

• jrmcornish / cif
  PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows
  ☆31Updated 3 years ago
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated last year
• wangleiphy / MongeAmpereFlow
  Continuous-time gradient flow for generative modeling and variational inference
  ☆30Updated 6 years ago
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 5 years ago
• CW-Huang / CP-Flow
  Convex potential flows
  ☆82Updated 3 years ago
• asteroidhouse / INN-exploding-inverses
  Code for Understanding and Mitigating Exploding Inverses in Invertible Neural Networks (AISTATS 2021) http://arxiv.org/abs/2006.09347
  ☆29Updated 4 years ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆51Updated 6 months ago
• ermongroup / mintnet
  MintNet: Building Invertible Neural Networks with Masked Convolutions
  ☆39Updated 4 years ago
• MichaelArbel / GeneralizedEBM
  ☆53Updated 6 months ago
• AWehenkel / Graphical-Normalizing-Flows
  Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…
  ☆45Updated last year
• locuslab / monotone_op_net
  Monotone operator equilibrium networks
  ☆51Updated 4 years ago
• necludov / iMCMC
  ☆22Updated 4 years ago
• akandykeller / SelfNormalizingFlows
  ☆67Updated 2 years ago
• AlexanderMath / fasth
  Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.
  ☆72Updated 6 months ago
• AVMCMC / AuxiliaryVariationalMCMC
  Code required to reproduce the experiments in Auxiliary Variational MCMC
  ☆17Updated 6 years ago
• cunningham-lab / igr
  ☆31Updated 4 years ago
• patrick-kidger / FasterNeuralDiffEq
  Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
  ☆86Updated 2 years ago
• ermongroup / higher_order_invariance
  Code for "Accelerating Natural Gradient with Higher-Order Invariance"
  ☆30Updated 5 years ago
• aboustati / vargrad
  Code accompanying VarGrad: A Low-Variance Gradient Estimator for Variational Inference
  ☆12Updated 4 years ago
• gd-zhang / noisy-K-FAC
  Natural Gradient, Variational Inference
  ☆29Updated 5 years ago
• glouppe / flowing-with-jax
  ☆15Updated 4 years ago
• larsmaaloee / BIVA
  BIVA: A Very Deep Hierarchy of Latent Variables forGenerative Modeling
  ☆29Updated 5 years ago
• wgrathwohl / LSD
  Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"
  ☆49Updated 4 years ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 3 years ago
• gebob19 / introduction_to_normalizing_flows
  Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'
  ☆25Updated 3 years ago
• point0bar1 / ebm-anatomy
  PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"
  ☆38Updated 6 months ago
• juntang-zhuang / TorchDiffEqPack
  ☆45Updated last year
• robert-giaquinto / gradient-boosted-normalizing-flows
  We got a stew going!
  ☆27Updated last year
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆17Updated 3 years ago
• pierreablin / ksddescent
  Kernel Stein Discrepancy Descent : a method to sample from unnormalized densities
  ☆21Updated 10 months ago