• Dr. Oliver Hinder: Gradient Descent for Solving Linear Programs

  • 2022/06/18
  • 再生時間： 41 分
  • ポッドキャスト

Dr. Oliver Hinder: Gradient Descent for Solving Linear Programs

無料で聴く

ポッドキャストの詳細を見る

  • サマリー

  • Oliver Hinder is an Assistant Professor in Industrial Engineering Department at University of Pittsburgh. Before that he was a visiting post-doc at Google in the Optimization and Algorithms group in New York and received his PhD in 2019 in Management Science and Engineering from Stanford working with professor Yinyu Ye. He studies local optimization, gradient descent, both convex and nonconvex problems, etc.

    We chat about Oliver moving to the U.S. from New Zealand to start his PhD at Stanford; we talk about some of his recent work on gradient descent methods for solving LPs accurately and how using restarts can benefit algorithms like these. Finally, we touch on automated parameter tuning in ML especially in Deep Learning which is being widely used in many applications.

    Check it out!
    続きを読む 一部表示

あらすじ・解説

Oliver Hinder is an Assistant Professor in Industrial Engineering Department at University of Pittsburgh. Before that he was a visiting post-doc at Google in the Optimization and Algorithms group in New York and received his PhD in 2019 in Management Science and Engineering from Stanford working with professor Yinyu Ye. He studies local optimization, gradient descent, both convex and nonconvex problems, etc.

We chat about Oliver moving to the U.S. from New Zealand to start his PhD at Stanford; we talk about some of his recent work on gradient descent methods for solving LPs accurately and how using restarts can benefit algorithms like these. Finally, we touch on automated parameter tuning in ML especially in Deep Learning which is being widely used in many applications.

Check it out!
続きを読む 一部表示

Dr. Oliver Hinder: Gradient Descent for Solving Linear Programsに寄せられたリスナーの声

カスタマーレビュー：以下のタブを選択することで、他のサイトのレビューをご覧になれます。

Audible.co.jp

Amazon.co.jp
レビューはまだありません。