all AI news
Meta Develops Dataset Pruning Technique for Scaling AI Training
Aug. 16, 2022, 1 p.m. | Anthony Alford
InfoQ - AI, ML & Data Engineering www.infoq.com
Researchers from Meta AI and Stanford University have developed a metric for pruning AI datasets which improves training scalability from a power-law to exponential-decay. The metric uses self-supervised learning and performs comparably to existing metrics which require more compute power.
By Anthony Alfordai ai training dataset deep learning meta ml & data engineering neural networks news pruning scaling scaling ai training
More from www.infoq.com / InfoQ - AI, ML & Data Engineering
Mistral Large Foundation Model Now Available on Amazon Bedrock
1 day, 16 hours ago |
www.infoq.com
Airbnb Open-Sources its ML Feature Platform Chronon
6 days, 12 hours ago |
www.infoq.com
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Enterprise AI Architect
@ Oracle | Broomfield, CO, United States
Cloud Data Engineer France H/F (CDI - Confirmé)
@ Talan | Nantes, France