stat-ml / dpp-dropout-uncertainty
Effective uncertainty estimation with decorellation and DPP mask for dropout
☆9Updated last year
Alternatives and similar repositories for dpp-dropout-uncertainty:
Users that are interested in dpp-dropout-uncertainty are comparing it to the libraries listed below
- Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks☆84Updated 3 years ago
- Library for active learning and uncertainty estimation in machine learning☆27Updated 2 years ago
- Sliced Wasserstein Generator☆23Updated 6 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆81Updated 7 months ago
- Code for paper "Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow"☆19Updated 4 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- The Deep Weight Prior, ICLR 2019☆44Updated 3 years ago
- Models and code for the ICLR 2020 workshop paper "Towards Understanding Normalization in Neural ODEs"☆16Updated 4 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Implementation of Normalizing flows on MNIST https://arxiv.org/abs/1505.05770☆14Updated 6 years ago
- [AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution☆37Updated 4 years ago
- An official PyTorch implementation of "Regression Prior Networks" for effective runtime uncertainty estimation.☆35Updated 4 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated 8 months ago
- Code for "On the Expressiveness of Approximate Inference in Bayesian Neural Networks"☆13Updated 3 years ago
- ☆16Updated 6 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 4 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Code for MSID, a Multi-Scale Intrinsic Distance for comparing generative models, studying neural networks, and more!☆51Updated 5 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆63Updated 4 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆80Updated 4 years ago
- Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561☆24Updated 3 years ago
- Very simple and short implementation of gradient boosting in 18 lines of code☆9Updated 4 years ago
- ☆82Updated last year
- Learning error bars for neural network predictions☆69Updated 5 years ago
- Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch☆50Updated 7 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Reusable BatchBALD implementation☆76Updated 11 months ago
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning☆92Updated 4 years ago