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Networks, Neural network, Neuroscience, Artificial neural network, Artificial intelligence, Algorithm

Archai can design your neural network with state-of-the-art neural architecture search (NAS)

On Oct 1, 2020
@erichorvitz shared
What’s the best neural architecture for a dataset & task? We’ve rolled out #Archai, a platform that can bring us closer to a science of neural architecture search #NAS https://t.co/jX4EUQhXDg⁩ #ICLR2021 @sytelus⁩ ⁦@debadeepta #dnn #ml ⁦@AzureML⁩ @MSFTResearch⁩ https://t.co/HR9kA3AdH8
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Microsoft Research is happy to announce the availability of Archai on GitHub. The open-source platform unifies standard NAS algorithms for easier adoption, reproducibility, & fair evaluation. Archai can design your neural network with state-of-the-art NAS.

www.microsoft.com
On Oct 1, 2020
@erichorvitz shared
What’s the best neural architecture for a dataset & task? We’ve rolled out #Archai, a platform that can bring us closer to a science of neural architecture search #NAS https://t.co/jX4EUQhXDg⁩ #ICLR2021 @sytelus⁩ ⁦@debadeepta #dnn #ml ⁦@AzureML⁩ @MSFTResearch⁩ https://t.co/HR9kA3AdH8
Open

Archai can design your neural network with state-of-the-art neural architecture search (NAS)

Archai can design your neural network with state-of-the-art neural architecture search (NAS)

Microsoft Research is happy to announce the availability of Archai on GitHub. The open-source platform unifies standard NAS algorithms for easier adoption, reproducibility, & fair ...

Why Initialize a Neural Network with Random Weights?

Why Initialize a Neural Network with Random Weights?

The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the ...

Auto-Keras and AutoML: A Getting Started Guide

Auto-Keras and AutoML: A Getting Started Guide

In this tutorial, you will learn about Auto-Keras and AutoML for automated machine learning and deep learning. You will also learn how to use the Auto-Keras Python package.

Using platform-aware AI to design compact and efficient neural networks

Using platform-aware AI to design compact and efficient neural networks

Researchers at Facebook, Princeton, and UC Berkeley have developed a method that uses AI to find and propose the most efficient design for neural networks based on how and where they'll ...

Weight Agnostic Neural Networks

Weight Agnostic Neural Networks

Networks that can already (sort of) perform tasks with random weights.

Project Petridish: Efficient forward neural architecture search

Project Petridish: Efficient forward neural architecture search

Finding a neural architecture for a new dataset is frustrating. Researchers aim to reduce the trial and error involved with Petridish, a NAS algorithm that employs forward search and builds ...

Xfer: an open-source library for neural network transfer learning

Xfer: an open-source library for neural network transfer learning

Transfer learning is a set of techniques for reusing and repurposing already trained machine learning models in new situations. It brings…

TPOT Automated Machine Learning in Python

TPOT Automated Machine Learning in Python

In this post I’m sharing some of my explorations with TPOT, an automated machine learning (autoML) tool in Python. The goal is to see what…

Neural Architecture and AutoML Technology

Neural Architecture and AutoML Technology

Machine learning (AutoML) has given some huge achievements in diverse fields as of late..Neural architecture search (NAS) has been touted as the way ahead for lightening this agony via ...

Amazon’s AutoGluon helps developers deploy deep learning models with just a few lines of code

Amazon’s AutoGluon helps developers deploy deep learning models with just a few lines of code

AutoGluon democratizes machine learning, and makes the power of deep learning available to all developers.

Neural Architecture Search

Neural Architecture Search

Neural Architecture Search (NAS) automates network architecture engineering. It aims to learn a network topology that can achieve best performance on a certain task. By dissecting the ...

A Brief History Of Neural Network Architectures

A Brief History Of Neural Network Architectures

Much of the effectiveness of deep learning comes from neural network architectures. Eugenio Culurciello tells the history of modern neural network design.

Automating the development of deep-learning-based predictive models for cancer research

Automating the development of deep-learning-based predictive models for cancer research

Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data.

Deep Learning Helps Argonne Create Cancer Research Models

Deep Learning Helps Argonne Create Cancer Research Models

Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data.

Generative Adversarial Networks – Key Milestones and State of the Art

Generative Adversarial Networks – Key Milestones and State of the Art

In the simplest GAN structure, the generator starts with random data and learns to transform this noise into information that matches the distribution of the real data. Researchers ...