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[R] Deep Double Descent: Where Bigger Models and More Data Hurt

See the OpenAI blog post and their paper.

Contrary to conventional wisdom, we find that the performance of CNNs, ResNets, and transformers is non-monotonic: it first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.