210 citations · 984 across the 29 of their papers we have counts for
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cs.LG2018★ 104 cited
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora, Nadav Cohen, Noah Golowich +1
We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as ) by minimizing t…
quant-ph2018★ 210 cited
Quantum Entanglement in Deep Learning Architectures
Yoav Levine, Or Sharir, Nadav Cohen +1
Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more tra…
cs.LG2018★ 124 cited
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan
Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can sp…