proxprop

Proximal Backpropagation

A neural network training algorithm that uses implicit gradient steps instead of explicit ones to update network parameters

Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

GitHub

41 stars
15 watching
6 forks
Language: Python
last commit: over 5 years ago

Related projects:

Repository Description Stars
mstksg/backprop A Haskell library providing automatic heterogeneous back-propagation for differentiable programming and deep learning applications. 181
lancopku/meprop A technique to simplify backpropagation in neural networks by selectively computing only the most relevant gradients 110
kimhc6028/forward-thinking-pytorch An implementation of a novel neural network training method that builds and trains networks one layer at a time. 65
fgxaos/pytorch-innvestigate PyTorch implementation of an explainability technique for deep neural networks 9
hjmshi/pytorch-lbfgs A PyTorch implementation of L-BFGS optimization algorithm for training neural networks 586
samsunglabs/fbrs_interactive_segmentation A framework for training and testing interactive segmentation models using PyTorch and supporting various architectures 583
backprop-ai/backprop A Python library that provides pre-trained models and tools for fine-tuning and deploying natural language processing tasks 243
alexis-jacq/pytorch-dppo A PyTorch implementation of Distributed Proximal Policy Optimization algorithm 180
probprog/pyprob A probabilistic programming system for simulators and high-performance computing based on PyTorch 27
akanimax/pro_gan_pytorch Implementation of a deep learning model for generating high-quality images with improved stability and variation. 536
ag14774/diffdist Enables backpropagation in distributed settings and facilitates model parallelism using differentiable communication between processes 61
saschagrunert/nn A small neural network implementation of the backpropagation algorithm in Haskell 127
hui-po-wang/progfed An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. 20
guopengf/auto-fedrl A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. 15
ybillchen/bp-neural-network-matlab An implementation of a basic backpropagation neural network using MATLAB 92