Python Stochastic-differential-equations Resources

Binary Stochastic Neurons in PyTorch http://r2rt.com/binary-stochastic-neurons-in-tensorflow.html https://github.com/pytorch/examples/tree/master/mnis

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis Andreas Bl

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient (paper) @misc{zhang2021compress,

DeepLM DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021) Run Please install th

Solve Volterra and Fredholm integral equations This Python package estimates Volterra and Fredholm integral equations using known techniques. Installa

federated is the source code for the Bachelor's Thesis Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federat

Stochastic Image-to-Video Synthesis using cINNs Official PyTorch implementation of Stochastic Image-to-Video Synthesis using cINNs accepted to CVPR202

Stochastic Normalizing Flows We introduce stochasticity in Boltzmann-generating flows. Normalizing flows are exact-probability generative models that

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

Differentiable Optimizers with Perturbations in PyTorch This contains a PyTorch implementation of Differentiable Optimizers with Perturbations in Tens

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie

torch-optimizer torch-optimizer -- collection of optimizers for PyTorch compatible with optim module. Simple example import torch_optimizer as optim

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha

Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not

Navier-Stokes-numerical-solution-using-Python- Python script for Linear, Non-Linear Convection, Burger’s & Poisson Equation in 1D & 2D, 1D D

ANUGA Contents ANUGA What is ANUGA? Installation Documentation and Help Mailing Lists Web sites Latest source code Bug reports Developer information L

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Gen

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A