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Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit


Jul 18, 2022
Boaz Barak, Benjamin L. Edelman, Surbhi Goel, Sham Kakade, Eran Malach, Cyril Zhang

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Knowledge Distillation: Bad Models Can Be Good Role Models


Mar 28, 2022
Gal Kaplun, Eran Malach, Preetum Nakkiran, Shai Shalev-Shwartz

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On the Power of Differentiable Learning versus PAC and SQ Learning


Aug 09, 2021
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro

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Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels


Mar 01, 2021
Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro

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The Connection Between Approximation, Depth Separation and Learnability in Neural Networks


Jan 31, 2021
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir

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Computational Separation Between Convolutional and Fully-Connected Networks


Oct 03, 2020
Eran Malach, Shai Shalev-Shwartz

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When Hardness of Approximation Meets Hardness of Learning


Aug 23, 2020
Eran Malach, Shai Shalev-Shwartz

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Learning Parities with Neural Networks


Feb 18, 2020
Amit Daniely, Eran Malach

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Proving the Lottery Ticket Hypothesis: Pruning is All You Need


Feb 03, 2020
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir

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