Alternatives and similar repositories for ece-kde

Users that are interested in ece-kde are comparing it to the libraries listed below

• omegafragger / DDU
  Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
  ☆143Updated 2 years ago
• Jonathan-Pearce / calibration-toolbox
  NumPy library for calibration metrics
  ☆73Updated 6 months ago
• torrvision / focal_calibration
  Code for the paper "Calibrating Deep Neural Networks using Focal Loss"
  ☆160Updated last year
• hollance / reliability-diagrams
  Reliability diagrams visualize whether a classifier model needs calibration
  ☆155Updated 3 years ago
• AIgen / df-posthoc-calibration
  Model-agnostic posthoc calibration without distributional assumptions
  ☆42Updated last year
• kartikgupta-at-anu / spline-calibration
  Code implementation of our ICLR'21 paper "Calibration of Neural Networks using Splines"
  ☆22Updated 2 years ago
• JakobCode / UncertaintyInNeuralNetworks_Resources
  ☆109Updated 4 years ago
• KaosEngineer / PriorNetworks
  ☆66Updated 5 years ago
• IntelLabs / AVUC
  Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.
  ☆54Updated 2 years ago
• p-lambda / verified_calibration
  Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…
  ☆150Updated 2 years ago
• uiuctml / ISR
  Invariant-feature Subspace Recovery (ISR)
  ☆23Updated 2 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…
  ☆24Updated 4 years ago
• geifmany / uncertainty_ICLR
  Code for the paper "Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers" published in ICLR 2019
  ☆14Updated 6 years ago
• IML-DKFZ / fd-shifts
  A Benchmark for Failure Detection under Distribution Shifts in Image Classification
  ☆33Updated 10 months ago
• y0ast / DUE
  Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".
  ☆112Updated 3 years ago
• YyzHarry / SubpopBench
  [ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
  ☆108Updated 2 years ago
• lisa-wm / entropybaseduq
  ☆10Updated 5 months ago
• markus93 / NN_calibration
  Calibration of Convolutional Neural Networks
  ☆170Updated 2 years ago
• dirichletcal / experiments_neurips
  ☆21Updated 2 years ago
• konstantinos-p / Bayesian-Neural-Networks-Reading-List
  A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesi…
  ☆56Updated last year
• alexrame / fishr
  Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization
  ☆87Updated 3 years ago
• PolinaKirichenko / deep_feature_reweighting
  ☆108Updated last year
• microsoft / Domain-Adaptation-with-Conditional-Distribution-Matching-and-Generalized-Label-Shift
  Code for the paper Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
  ☆38Updated 4 years ago
• cambridge-mlg / cifar-10s
  HCOMP '22 -- Eliciting and Learning with Soft Labels from Every Annotator
  ☆10Updated 2 years ago
• fregu856 / regression_uncertainty
  Official implementation of "How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?", TMLR 2023.
  ☆18Updated last year
• bmucsanyi / untangle
  Large-scale uncertainty benchmark in deep learning.
  ☆63Updated 3 months ago
• p3i0t / SimCLR-CIFAR10
  Pytorch SimCLR on CIFAR10 (92.85% test accuracy)
  ☆58Updated 5 years ago
• benchopt / benchmark_resnet_classif
  Benchopt benchmark for ResNet fitting on a classification task
  ☆12Updated last year
• mlfoundations / tableshift
  A benchmark for distribution shift in tabular data
  ☆55Updated last year
• aangelopoulos / ltt
  Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
  ☆68Updated 9 months ago