Alternatives and similar repositories for SDE-Net

Users that are interested in SDE-Net are comparing it to the libraries listed below

• YoungseogChung / calibrated-quantile-uq
  Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)
  ☆43Updated 10 months ago
• tung-nd / TNP-pytorch
  Official implementation of Transformer Neural Processes
  ☆78Updated 3 years ago
• juho-lee / bnp
  Pytorch implementation of neural processes and variants
  ☆29Updated last year
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 5 years ago
• tdmeeste / TimeAwareRNN
  Code used for the AAAI 2020 paper "System Identification with Time-Aware Neural Sequence Models"
  ☆17Updated 5 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆34Updated 3 years ago
• LaurenceA / bayesfunc
  ☆15Updated 3 years ago
• a-norcliffe / sonode
  Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"
  ☆60Updated 11 months ago
• mhw32 / meta-inference-public
  A PyTorch implementation of "Meta-Amortized Variational Inference and Learning" (https://arxiv.org/abs/1902.01950)
  ☆14Updated 5 years ago
• jambo6 / online-neural-cdes
  Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"
  ☆38Updated 2 years ago
• john-x-jiang / meta_ssm
  Repository for ICLR 2023 work, "Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting"
  ☆31Updated last year
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• Shen-Lab / Bayesian-L2O
  [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…
  ☆14Updated 3 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆74Updated 3 years ago
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆53Updated 5 years ago
• changliu00 / AWGF
  Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)
  ☆22Updated 5 years ago
• chenw20 / SGPA
  Example code of Sparse Gaussian Process Attention (ICLR 2023)
  ☆26Updated last week
• clear-nus / NCDSSM
  PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularl…
  ☆25Updated 2 years ago
• ChengzijunAixiaoli / PPMM
  Python3 implementation of the paper [Large-scale optimal transport map estimation using projection pursuit]
  ☆15Updated 4 years ago
• Lingkai-Kong / SDE-Net
  Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
  ☆115Updated 4 years ago
• zhougroup / BAM
  Bayesian Attention Modules
  ☆35Updated 4 years ago
• thjashin / rodeo
  Gradient Estimation with Discrete Stein Operators (NeurIPS 2022)
  ☆17Updated last year
• mstaib / mmd-dro-code
  Accompanying code for our NeurIPS 2019 paper
  ☆12Updated 5 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆43Updated 3 months ago
• team-approx-bayes / fromp
  Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"
  ☆44Updated 4 years ago
• StanfordASL / moca
  ☆32Updated 2 years ago
• ae-foster / pyro
  Deep universal probabilistic programming with Python and PyTorch
  ☆12Updated 5 years ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• federicobergamin / Variational-Inference-with-Normalizing-Flows
  Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch
  ☆22Updated 5 years ago