PyTorch implementation of Neural Optimizer Search's Optimizer_1
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Updated
Aug 21, 2017 - Python
PyTorch implementation of Neural Optimizer Search's Optimizer_1
Minimal Tensorflow implementation of the paper "Neural Architecture Search With Reinforcement Learning" presented at ICLR 2017
PyTorch implementation of AddSign and PowerSign optimizers presented in 'Neural Optimizer Search with Reinforcement Learning'
Re-implementation of Neural Architecture Search using Reinforcement Learning
Differentiable neural architecture search
Keras implementation of Efficient Neural Architecture Search
Record the learning process.
Progressive Neural Architecture Search coupled with Binarized CNNs to search for resource efficient and accurate architectures.
Implementing the update rule found in Neural Optimizer search with Reinforcement learning
Neural-Architecture-Search-Using-Genetic-Algorithm
TensorFlow implementation of PNASNet-5 on ImageNet
A greedy approach for finding optimal architecture for Multi-Task Learning. Deprecated (see https://github.com/hav4ik/Hydra)
The state-of-the-art algorithms on CIFAR-10 dataset in PyTorch
Efficient Neural Architecture Search coupled with Quantized CNNs to search for resource efficient and accurate architectures.
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Evolutionary YOLO
Interactive framework for testing and building machine learning projects.
Implement Differentiable Architecture Search (DARTS) for RNN with fastai
Learnable Embedding Space for Efficient Neural Architecture Compression
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