Every reasonable ML technique has some sort of mathematical… - Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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Every reasonable ML technique has some sort of mathematical guarantee. For example, neural nets have a finite VC dimension, hence they are consistent and have generalization bounds... every single bound is terrible and useless in practice. As long as your method minimizes some sort of objective function and has a finite capacity (or is properly regularized), you are on solid theoretical grounds.

Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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About Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

Yann André LeCun (originally spelled Le Cun; born 8 July 1960) is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta. LeCun received the 2018 Turing Award, together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning.

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Alternative Names: #258 – Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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Every reasonable ML technique has some sort of mathematical guarantee. For example, neural nets have a finite VC dimension, hence they are consistent… - Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

About Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning

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