izmailovpavel / bnn_covariate_shift
Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
☆33Updated 2 years ago
Alternatives and similar repositories for bnn_covariate_shift:
Users that are interested in bnn_covariate_shift are comparing it to the libraries listed below
- ☆53Updated 8 months ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆35Updated 2 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆43Updated 2 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆88Updated 4 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆83Updated 4 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆75Updated last year
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated 11 months ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Last-layer Laplace approximation code examples☆83Updated 3 years ago
- Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …☆39Updated 4 years ago
- Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.☆16Updated 3 years ago
- Limitations of the Empirical Fisher Approximation☆47Updated last month
- ☆66Updated 6 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆19Updated 3 years ago
- PyTorch implementation of Stein Variational Gradient Descent☆43Updated last year
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆57Updated 3 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆82Updated 10 months ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- Distributional and Outlier Robust Optimization (ICML 2021)☆26Updated 3 years ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 5 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆31Updated 3 years ago
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- Bayesian active learning with EPIG data acquisition☆31Updated last week