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The direction of history is that the more data we get, the more our methods rely on learning. Ultimately, the task use learning end to end. That's wh… - Yann LeCun
" "The direction of history is that the more data we get, the more our methods rely on learning. Ultimately, the task use learning end to end. That's what happened for speech, handwriting, and object recognition. It's bound to happen for NLP.
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About Yann LeCun
Yann André LeCun (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:
LeCun
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YL Cun
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Yann L. Cun
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Y. LeCun
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Y LeCun
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Yann André LeCun
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Yann André Le Cun
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Y. Le Cun
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Yann A. LeCun
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Yann Andre LeCun
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Yann Andre Le Cun
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Additional quotes by Yann LeCun
I try to stay away from all methods that require sampling. I must have an allergy of some sort. That said, I am neither Bayesian nor anti-Bayesian... I think Bayesian methods are really cool conceptually in some cases... but I really don't have much faith in things like non-parametric Bayesian methods...
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.
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