A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
This release includes support for quantization of all the Bayesian Convolutional layers listed below in addition to Conv2dReparameterization and Conv2dFlipout.
Conv1dReparameterization, Conv3dReparameterization, ConvTranspose1dReparameterization, ConvTranspose2dReparameterization, ConvTranspose3dReparameterization, Conv1dFlipout, Conv3dFlipout, ConvTranspose1dFlipout, ConvTranspose2dFlipout, ConvTranspose3dFlipout
This release also includes the fixes for the following issues: Issue https://github.com/IntelLabs/bayesian-torch/issues/27 Issue https://github.com/IntelLabs/bayesian-torch/issues/21 Issue https://github.com/IntelLabs/bayesian-torch/issues/24 Issue https://github.com/IntelLabs/bayesian-torch/issues/34
Full Changelog: https://github.com/IntelLabs/bayesian-torch/compare/v0.4.0...v0.5.0
This release introduces Quantization framework for Bayesian neural networks in Bayesian-Torch. Support Post Training Quantization of Bayesian deep neural networks, enables optimized and efficient INT8 inference on Intel platforms.
Full Changelog: https://github.com/IntelLabs/bayesian-torch/compare/v0.3.0...v0.4.0
support arbitrary kernel sizes in the Bayesian convolutional layers
Includes dnn_to_bnn new feature: An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition i.e. drop-in replacements of Convolutional, Linear and LSTM layers to corresponding Bayesian layers. This will enable seamless conversion of existing topology of larger models to Bayesian deep neural network models for extending towards uncertainty-aware applications.
Includes dnn_to_bnn new feature: An API to convert deterministic deep neural network (dnn) model of any architecture to Bayesian deep neural network (bnn) model, simplifying the model definition i.e. drop-in replacements of Convolutional, Linear and LSTM layers to corresponding Bayesian layers. This will enable seamless conversion of existing topology of larger models to Bayesian deep neural network models for extending towards uncertainty-aware applications.
Full Changelog: https://github.com/IntelLabs/bayesian-torch/compare/v0.1...v0.2.0