Scaled Machine Learning

Stanford University
August 2nd 2016, 8:30am - 6:00pm

Machine Learning is evolving to utilize new hardware such as GPUs and large commodity clusters. University and industry researchers have been using these new computing platforms to scale machine learning across many dimensions.

This conference aims to bring together researchers running machine learning algorithms on a variety of computing platforms to foster discussions between them. The goal is to encourage algorithm designers for these platforms to help each other scale and transplant ideas between the platforms.

Speakers (confirmed)

  • Qi Lu (Microsoft)
    Machine Learning at Microsoft
  • Jeff Dean (Google)
    TensorFlow and its capabilities
  • Ilya Sutskever (OpenAI)
    Recent Progress in Generative Modelling
  • Reza Zadeh (Stanford and Matroid)
    Scaled ML at Matroid
  • John Canny (UC Berkeley and Google)
    GPU acceleration for Machine Learning
  • Lise Getoor (UCSC)
    Scalable ML for Graphs
  • Ted Dunning (MapR)
    ML at MapR
  • Leah McGuire (Salesforce)
    The Machine Learning Platform at Salesforce
  • Xavier Amatriain (Quora)
    Scaled Machine Learning at Quora
  • Martin Wicke (Google)
    A Tutorial on TensorFlow


Tuesday August 2nd 2016
08:45-09:00 Reza Zadeh, Introduction

09:00-10:00 Qi Lu (Keynote), Machine Learning at Microsoft
10:00-10:30 Reza Zadeh
10:30-11:00 Ilya Sutskever
11:00-11:30 Ted Dunning
11:30-12:00 Leah McGuire

12:00-13:00 Lunch Break (Lunch provided courtesy of Matroid)

13:00-14:00 Jeff Dean (Keynote), Deep Dive into TensorFlow
14:00-14:30 Lise Getoor
14:30-15:00 John Canny
15:00-15:30 Xavier Amatriain

15:30-15:50 Coffee and Tea Break (Provided courtesy of Matroid)

15:50-17:50 Martin Wicke, A tutorial on TensorFlow


Slides from the event.


Please register here.

Directions and Parking

The meeting is in Arrilaga Alumni Center, on Stanford University campus. The exact address is:

Frances C. Arrillaga Alumni Center
326 Galvez Street
Stanford, CA 94305-6105

Parking instructions and directions from airports and other transporations options are listed on the alumni center webpage.

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