ohmnet
Feature learner
An algorithm for learning feature representations in multi-layer networks
OhmNet: Representation learning in multi-layer graphs
81 stars
8 watching
33 forks
Language: Python
last commit: over 4 years ago
Linked from 1 awesome list
bioinformaticsdeep-learningfeature-learninggenomicsmulti-layerneural-embeddings
Related projects:
Repository | Description | Stars |
---|---|---|
| Predicts side effects of drug combinations by learning node embeddings in multimodal graphs using graph convolutional networks | 452 |
| Provides MATLAB code and dataset for training machine learning models in millimeter wave and massive MIMO systems | 162 |
| A repository providing MatLab/Octave examples and explanations of popular machine learning algorithms | 857 |
| Investigating neural networks for drug discovery using multiple chemical descriptors. | 3 |
| A comprehensive resource for machine learning and deep learning algorithms | 295 |
| A Python machine learning library providing efficient array operations and neural network functionality | 3 |
| A lightweight implementation of a multilayer perceptron neural network for use in embedded systems and microcontrollers | 183 |
| Datasets and code for machine learning in 5G mmWave MIMO systems involving mobility | 89 |
| A Ruby implementation of common machine learning algorithms and techniques | 13 |
| A high-level machine learning library built on Gorgonia for Go that aims to provide an easy-to-use interface for building and training neural networks. | 373 |
| A course project on parallel computing and scientific machine learning using Julia programming language | 226 |
| Automates the search for optimal neural network configurations in deep learning applications | 468 |
| A PyTorch module that adds differentiable optimization as a layer to neural networks | 517 |
| Developing a high-performance machine learning library that balances speed and flexibility in Haskell | 1,623 |
| This project implements a deep metric learning framework using an adversarial auxiliary loss to improve robustness. | 39 |