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#86 - Prof. YANN LECUN and Dr. RANDALL BALESTRIERO - SSL, Data Augmentation, Reward isn't enough [NEURIPS2022]

Machine Learning Street Talk (MLST)

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Intro

This chapter examines the impact of recent NeurIPS papers on unitary neural networks and their ability to overcome common training challenges like exploding and vanishing gradients. It also explores innovative data augmentation techniques, focusing on the transformative potential of image modifications for improving model performance.

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