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
41 stars
15 watching
6 forks
Language: Python
last commit: almost 6 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A Haskell library providing automatic heterogeneous back-propagation for differentiable programming and deep learning applications. | 180 |
| A technique to simplify backpropagation in neural networks by selectively computing only the most relevant gradients | 110 |
| An implementation of a novel neural network training method that builds and trains networks one layer at a time. | 66 |
| PyTorch implementation of an explainability technique for deep neural networks | 9 |
| A PyTorch implementation of L-BFGS optimization algorithm for training neural networks | 591 |
| A framework for training and testing interactive segmentation models using PyTorch and supporting various architectures | 583 |
| A Python library that provides pre-trained models and tools for fine-tuning and deploying natural language processing tasks | 243 |
| A PyTorch implementation of Distributed Proximal Policy Optimization algorithm | 180 |
| A probabilistic programming system for simulators and high-performance computing based on PyTorch | 27 |
| Implementation of a deep learning model for generating high-quality images with improved stability and variation. | 538 |
| Enables backpropagation in distributed settings and facilitates model parallelism using differentiable communication between processes | 62 |
| A small neural network implementation of the backpropagation algorithm in Haskell | 127 |
| An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
| A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. | 15 |
| An implementation of a basic backpropagation neural network using MATLAB | 94 |