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![Archived Post ] ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA | by Jae Duk Seo | Medium](https://miro.medium.com/max/1400/1*LcWTNyTqObKjcZU3McCCQg.png "Archived Post ] ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA | by Jae Duk Seo | Medium")
Archived Post ] ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA | by Jae Duk Seo | Medium
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Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide | npj Computational Materials
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide | npj Computational Materials
A foolproof way to shrink deep learning models | MIT News | Massachusetts Institute of Technology
Rustici Software (@rusticisoftware) / Twitter
Machine Learning and Trading. Gap Plays | by Dhruv Laad | Analytics Vidhya | Medium
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Too many machine learning papers? | The Data Mining Blog
PEPR '20 Conference Program | USENIX
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide | npj Computational Materials
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide | npj Computational Materials
Application and theory gaps during the rise of Artificial Intelligence in Education - ScienceDirect
Machine Learning 2020 summary: 84 interesting papers/articles | by Akihiro FUJII | Towards Data Science
A Closer Look at the Generalization Gap in Large Batch Training of Neural Networks | Synced
Learning from User Feedback for Machine Translation in Real-Time | SpringerLink
The confidence gap predicts the gender pay gap among STEM graduates | PNAS
A Closer Look at the Generalization Gap in Large Batch Training of Neural Networks | Synced
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide | npj Computational Materials
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AI, ML, & Distributed Computing at rstudio::global(2021) - Industry - RStudio Community
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