RandWireNN
by seungwonpark
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"
AI summary
Neural explorer
Implementation of neural network exploration with randomly wired architectures
- stars
- 685
- forks
- 135
- watching
- 25
Similar projects
Found by comparing what the projects do, not just their names.
Neural search algorithm
An implementation of Neural Architecture Search using Reinforcement Learning with a Controller RNN.
Neural net architecture
A PyTorch implementation of a ShuffleNet-v2 neural network architecture for image classification.
Neural network papers
A curated collection of research papers on neural networks and deep learning
SNN framework
A Python library for building and deploying signal processing applications with spiking neural networks on various hardware platforms.
Neural net architecture
An implementation of a PyTorch-based neural network architecture for image classification tasks.
Neural network architecture
An implementation of a 1-bit weight neural network architecture using PyTorch
Neural network framework
An implementation of neural network components and optimization methods for text analysis, including rationales for neural predictions.
Neural Network Playground
Provides a learning environment to explore neural network concepts using Swift
Neural network architecture
A Torch implementation of a novel neural network architecture designed to improve the generalization ability of deep image classification models.
Neural network architecture
Represents a neural network architecture with 1K layers, designed for image recognition tasks.
Mobile CNN
An implementation of a computationally efficient deep neural network architecture designed for mobile devices with limited computing power.
Network tuner
Automates the search for optimal neural network configurations in deep learning applications
CNN model
An implementation of a lightweight convolutional neural network architecture for mobile devices
Network libraries
A collection of pre-trained neural network models with simple interfaces for easy integration into machine learning workflows.
Neural network potential
A neural network potential for atomistic modeling
