Alternatives and similar repositories for Neural-Process-Family

Users that are interested in Neural-Process-Family are comparing it to the libraries listed below

• EmilienDupont / neural-processes
  Pytorch implementation of Neural Processes for functions and images
  ☆230Updated 3 years ago
• wesselb / neuralprocesses
  A framework for composing Neural Processes in Python
  ☆85Updated 5 months ago
• cambridge-mlg / convcnp
  Implementation of the Convolutional Conditional Neural Process
  ☆123Updated 4 years ago
• TyXe-BDL / TyXe
  ☆153Updated 2 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 4 years ago
• automl / TransformersCanDoBayesianInference
  Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper
  ☆217Updated 7 months ago
• gpapamak / maf
  Masked Autoregressive Flow
  ☆213Updated 10 months ago
• wassname / attentive-neural-processes
  implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
  ☆94Updated 2 months ago
• izmailovpavel / understandingbdl
  ☆241Updated 2 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆72Updated 3 years ago
• johannbrehmer / manifold-flow
  Manifold-learning flows (ℳ-flows)
  ☆229Updated 4 years ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• BorealisAI / continuous-time-flow-process
  PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)
  ☆48Updated 4 years ago
• AWehenkel / UMNN
  Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for mod…
  ☆118Updated 5 months ago
• soobinseo / Attentive-Neural-Process
  A Pytorch Implementation of Attentive Neural Process
  ☆74Updated 6 years ago
• 000Justin000 / torchdiffeq
  Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
  ☆59Updated 4 years ago
• ex4sperans / variational-inference-with-normalizing-flows
  Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
  ☆234Updated 6 years ago
• LukasRinder / normalizing-flows
  Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
  ☆147Updated last week
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• didriknielsen / survae_flows
  Code for paper "SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows"
  ☆287Updated 4 years ago
• tung-nd / TNP-pytorch
  Official implementation of Transformer Neural Processes
  ☆76Updated 2 years ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆101Updated last year
• VectorInstitute / vbll
  Simple (and cheap!) neural network uncertainty estimation
  ☆66Updated last week
• ratschlab / GP-VAE
  TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155
  ☆141Updated 2 years ago
• tonyduan / normalizing-flows
  Neural Spline Flow, RealNVP, Autoregressive Flow, 1x1Conv in PyTorch.
  ☆279Updated last year
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆125Updated 7 months ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆128Updated 9 months ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆84Updated 4 years ago
• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆121Updated 3 years ago
• wesselb / stheno
  Gaussian process modelling in Python
  ☆223Updated 5 months ago