Skip to content
#

kronecker-factored-approximation

Star

Here are 3 public repositories matching this topic...

Language: All
Filter by language
All 3 Jupyter Notebook 1 Python 1

ikostrikov / pytorch-a2c-ppo-acktr-gail

Star 3.5k

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

reinforcement-learning deep-learning deep-reinforcement-learning pytorch atari hessian second-order continuous-control actor-critic ale mujoco proximal-policy-optimization ppo advantage-actor-critic a2c acktr natural-gradients roboschool kfac kronecker-factored-approximation
  • Updated May 29, 2022
  • Python

JavierAntoran / Bayesian-Neural-Networks

Star 1.8k

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

deep-learning reproducible-research regression pytorch uncertainty classification uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation mc-dropout bayes-by-backprop out-of-distribution-detection sgld
  • Updated Oct 20, 2023
  • Jupyter Notebook

konstantinos-p / Bayesian-Neural-Networks-Reading-List

Star 35

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

deep-learning uncertainty uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation bayes-by-backprop sgld
  • Updated Nov 1, 2023

Improve this page

Add a description, image, and links to the kronecker-factored-approximation topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the kronecker-factored-approximation topic, visit your repo's landing page and select "manage topics."

Learn more