Alternatives and similar repositories for phd-thesis

Users that are interested in phd-thesis are comparing it to the libraries listed below

• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• pierreablin / ksddescent
  Kernel Stein Discrepancy Descent : a method to sample from unnormalized densities
  ☆21Updated last year
• f-dangel / vivit
  [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivative…
  ☆17Updated last year
• AmirTag / OT-ICNN
  Learning the optimal transport map via input convex neural neworks
  ☆41Updated 5 years ago
• aterenin / SparseGPAX
  An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.
  ☆22Updated 4 years ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• wesselb / NeuralProcesses.jl
  A framework for composing Neural Processes in Julia
  ☆76Updated 4 years ago
• aterenin / SparseGaussianProcesses.jl
  A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.
  ☆33Updated 4 years ago
• probabilistic-numerics / probnum-spring-school
  Tutorial materials of the Probabilistic Numerics Spring School.
  ☆34Updated 2 years ago
• markvdw / RobustGP
  Robust initialisation of inducing points in sparse variational GP regression models.
  ☆33Updated 2 years ago
• pmorenoz / RecyclableGP
  Recyclable Gaussian Processes
  ☆11Updated 2 years ago
• robert-giaquinto / gradient-boosted-normalizing-flows
  We got a stew going!
  ☆27Updated last year
• Information-Fusion-Lab-Umass / NuX
  Normalizing Flows using JAX
  ☆83Updated last year
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated 2 years ago
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 5 years ago
• pmorenoz / ModularGP
  Modular Gaussian Processes
  ☆15Updated 3 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 6 years ago
• ialong / GPt
  Gaussian Processes for Sequential Data
  ☆18Updated 4 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆19Updated 3 years ago
• RussellTsuchida / snefy
  An implementation of squared neural families in PyTorch
  ☆14Updated 8 months ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆32Updated 3 years ago
• DanieleGammelli / pyro-torch-normalizing-flows
  Implementations of Normalizing Flows in Pytorch/Pyro
  ☆19Updated 5 years ago
• izmailovpavel / bnn_covariate_shift
  Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
  ☆33Updated 2 years ago
• pnkraemer / tornadox
  Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…
  ☆21Updated 11 months ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 3 years ago
• patrick-kidger / FasterNeuralDiffEq
  Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
  ☆87Updated 2 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆72Updated 3 years ago
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• MJHutchinson / ExtrinsicGaugeIndependentVectorGPs
  A library implementing the kernels for and experiments using extrinsic gauge equivariant vector field Gaussian Processes
  ☆25Updated 3 years ago