Alternatives and similar repositories for NOMU:

Users that are interested in NOMU are comparing it to the libraries listed below

• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 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…
  ☆13Updated 2 years ago
• MJ10 / DEUP
  Code for experiments to learn uncertainty
  ☆30Updated 2 years ago
• ifiaposto / Distance_Aware_Bottleneck
  Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024
  ☆28Updated 6 months ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆19Updated 3 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆21Updated 4 years ago
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆23Updated 2 years ago
• nick11roberts / XD
  ☆11Updated 2 years ago
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆35Updated 2 years ago
• YannDubs / Invariant-Self-Supervised-Learning
  Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"
  ☆40Updated 2 years ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• dh7401 / T-Cal
  ☆10Updated 3 years ago
• lawrennd / neurips2014
  Notebooks for managing NeurIPS 2014 and analysing the NeurIPS experiment.
  ☆11Updated 10 months ago
• locuslab / orthogonal-convolutions
  Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness
  ☆44Updated 3 years ago
• kashif / uncertainty-metrics
  Welcome to Uncertainty Metrics! The goal of this library is to provide an easy-to-use interface for both measuring uncertainty across Goo…
  ☆22Updated 4 years ago
• izmailovpavel / bnn_covariate_shift
  Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
  ☆33Updated 2 years ago
• drewprinster / conformal-mfcs
  Code for multistep feedback covariate shift conformal prediction experiments in "Conformal Validity Guarantees Exist for Any Data Distrib…
  ☆25Updated 9 months ago
• Sulam-Group / k-rcps
  "How to Trust Your Diffusion Models: A Convex Optimization Approach to Conformal Risk Control"
  ☆17Updated last month
• cambridge-mlg / CLUE
  Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
  ☆35Updated 11 months ago
• YoungseogChung / calibrated-quantile-uq
  Training quantile models
  ☆43Updated 4 months ago
• boschresearch / GMM_DAE
  ☆20Updated 3 years ago
• jlko / active-testing
  Active and Sample-Efficient Model Evaluation
  ☆24Updated 4 years ago
• RussellTsuchida / snefy
  An implementation of squared neural families in PyTorch
  ☆11Updated 5 months ago
• automl / PFNs4BO
  The official implementation of PFNs4BO: In-Context Learning for Bayesian Optimization
  ☆26Updated last year
• AhmedImtiazPrio / grok-adversarial
  Deep Networks Grok All the Time and Here is Why
  ☆33Updated 10 months ago
• yding5 / AdaptiveBinning
  Adaptive-binning for evaluation of confidence calibration
  ☆12Updated 5 years ago
• IdanAchituve / GDKL
  Code that accompanies the paper Guided Deep Kernel Learning
  ☆10Updated last year
• AaltoML / PeriodicBNN
  Code for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)
  ☆18Updated 3 years ago
• sjunhongshen / DASH
  ☆22Updated 2 years ago
• allenbai01 / transformers-as-statisticians
  ☆29Updated last year