JonathanWenger / pycalibLinks
Non-Parametric Calibration for Classification (AISTATS 2020)
☆19Updated 3 years ago
Alternatives and similar repositories for pycalib
Users that are interested in pycalib are comparing it to the libraries listed below
Sorting:
- ☆43Updated 6 years ago
- Python package for evaluating model calibration in classification☆20Updated 5 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated last year
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- ☆32Updated 7 years ago
- SGD and Ordered SGD codes for deep learning, SVM, and logistic regression☆35Updated 4 years ago
- Model-agnostic posthoc calibration without distributional assumptions☆42Updated last year
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 3 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆27Updated 4 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 7 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆150Updated 2 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- An Empirical Study of Invariant Risk Minimization☆27Updated 5 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 2 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Implementation of the Functional Neural Process models☆43Updated 5 years ago
- Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893☆89Updated 5 years ago
- Last-layer Laplace approximation code examples☆82Updated 3 years ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆92Updated 4 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 6 years ago
- ☆53Updated 7 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆86Updated 5 years ago
- ☆63Updated 4 years ago
- ☆13Updated 7 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆22Updated 5 years ago
- Implementation of Information Dropout☆39Updated 8 years ago