Jack D. Hidary

Optimization & Training · 2017

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jack D. Hidary trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Elisabeth Köbis (in Optimization & Training)

Papers from Jack D. Hidary's perspective

Ordered by how strongly the researchers Jack D. Hidary trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Understanding deep learning requires rethinking generalization
  2. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
  3. The Implicit Bias of Gradient Descent on Separable Data
  4. Train faster, generalize better: Stability of stochastic gradient descent
  5. Neural Network Learning: Theoretical Foundations
  6. Sharp Minima Can Generalize For Deep Nets
  7. Identity Matters in Deep Learning
  8. On Robust Optimization
  9. On Early Stopping in Gradient Descent Learning
  10. Neural networks and principal component analysis: Learning from examples without local minima

Their papers