AI Technology & Industry Review
University of Illinois Urbana-Champaign and Google researchers introduce AutoDistill, an end-to-end fully automated model distillation framework that integrates model architecture exploration and multi-objective optimization for building hardware-efficient pretrained natural language processing models.
A DeepMind research team proposes Faster Improvement Rate PBT (FIRE PBT) for Population Based Training (PBT), an automated hyperparameter tuning method for neural network training. The novel approach achieves faster improvement rates and better long-term performance.
A recent study by the Google Brain Team proposes a new way of programming automated machine learning (AutoML) based on symbolic programming.
Researchers from Tsinghua University have developed an AutoML framework and toolkit specifically designed for graph datasets and tasks.
A team of researchers from Shanghai Jiao Tong University (SJTU) has built a lightweight benchmark suite designed to help make life-saving models of medical image analysis even stronger.
Google researchers propose an “AutoML-Zero” approach designed to automatically search for machine learning (ML) algorithms from scratch, requiring minimal human expertise or input.
AutoGluon is designed to be an easy-to-use and easy-to-extend AutoML toolkit, suitable for both machine learning beginners and experts.
To help users design and tune machine learning models, neural network architectures or complex system parameters in an efficient and automatic way, in 2017 Microsoft Research began developing its Neural Network Intelligence (NNI) AutoML toolkit, open-sourcing v1.0 version in 2018.
A group of MIT researchers (Han Cai, Chuang Gan and Song Han) have introduced a “Once for All” (OFA) network that achieves the same or better level accuracy as state-of-the-art AutoML methods on ImageNet, with a significant speedup in training time.
Thanks to the creation of AutoML — which is essentially automated neural architecture search (NAS) — AI can now design better deep neural networks than human researchers for computer vision tasks such as image classification and object detection.
MIT and Brown have now upgraded the Northstar platform with an AutoML-based component called Virtual Data Scientist (VDS), which helps users generate machine learning models to run prediction tasks on datasets.
Might there be a more efficient approach to scaling up CNNs to improve accuracy? Researchers from Google AI say “yes” and have proposed a new model scaling method in their ICML 2019 paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
Automated machine learning (AutoML) is a hot topic in artificial intelligence. Researchers from German digital and software company USU Software AG and the University of Stuttgart recently published a review paper summarizing the latest academic and industrial developments in AutoML.
Designing accurate and efficient CNNs for mobile devices is challenging due to the large design space and expensive computational methods. Although many mobile CNNs are available for developers to train and deploy to mobile devices, existing CNN architecture may not be able to achieve the best results for some tasks on mobile devices.
The Synced Lunar New Year Project is a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this second installment (click here to read the previous article on Clarifai CEO Matt Zeiler), Synced speaks with Google Brain Researcher Quoc Le on his latest invention, AutoML, Google Brain’s pursuit of AI, and the secret of transforming lab technologies into real practices.
To make ML-based solutions available for a wider variety of deployment scenarios, Waymo’s autonomous driving team has collaborated with Google AI Brain Team researchers on a system that automates the creation of high quality and low latency neural networks on existing AutoML architectures.
Enter DarwinAI, a Waterloo, Ontario based AI startup which recently released a beta version of an automated machine learning solution it says can generate models ten times more efficiently than comparable state-of-the-art solutions.
Researchers from MIT, Google, and Xian Jiaotong University recently published a paper proposing AutoML for Model Compression (AMC), which leverages reinforcement learning to shorten model compression processing time and improve results.
At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products.
Google Cloud Chief Scientist Fei-Fei Li is one of the most popular and influential AI figures today. The woman behind the large-scale image dataset Image.Net is a visionary and an authority on AI’s development.
Who is using AutoML Vision, the “simple, secure and flexible ML service that lets you train custom vision models for your own use cases” that Google launched last month?
NVIDIA Announces Delivery Target For Self Driving Processor Xavier; Facebook Plans to Shut Down Its Personal Assistant “M” on January 19th; Amazon Alexa Is Integrating With Windows 10; A US Media Team Plans to Launch Vital Intelligence Data Live
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