User Tools

Site Tools



I am a research scientist with broad interests in machine learning and artificial intelligence. My work on large scale learning and stochastic gradient algorithms has received attention in the recent years. I am also known for the DjVu document compression system. I joined Facebook AI Research in March 2015.

Use the sidebar to navigate this site.


Learning Semantics


Learning Semantics,  Nips 2011 Workshop, Saturday December 17, 2011.  Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain.

This workshop is organized in collaboration with Antoine Bordes, Jason Weston, Ronan Collobert. This event should be very interesing: I believe that recent machine learning advances indicate new connections between machine learning and machine reasoning and lead to new opportunties for learning the semantics of the world.

2011/08/31 20:50 · leonb

From machine learning to machine reasoning

Over the last couple of years, I progressively formulated an unusual idea about the connection between machine learning and machine reasoning. I have discussed this idea with many friends and I even gave a seminar in Montreal in 2008. It is described in this technical report.

→

2011/02/09 03:13

On the Vapnik-Chevonenkis-Sauer lemma

Many machine learning authors write that a certain fundamental combinatorial result was independently established by Vapnik and Chervonenkis (1971), Sauer (1972), Shelah (1972), and sometimes Perles and Shelah (reference unknown). Vapnik and Chervonenkis published a version of their results in the Proceedings of the USSR Academy of Sciences four years earlier in 1968. It also appears that Sauer and Shelah pursued this result for very different purposes.

→

2010/12/20 12:52 · leonb


Patrice Simard and I have been friends since the old AT&T Bell Labs times. He eventually convinced me to work for him at Microsoft. He told me to expect “interesting times”.

I can see several reasons for these interesting times.

  • The scientific point of view. There are few places where I can find machine learning problems with similar scale, similar challenges, and similar impact. This practical experience will surely feed my future machine learning research. In fact I believe that such experiences are necessary to do research. One needs to see the world…
  • The social point of view. The Internet is the largest encyclopedia of knowledge ever known to mankind, and this is great. On the other hand, everything you do on the Internet is recorded by someone somewhere. Large online services such as Google or Microsoft concentrate unprecedented amounts of such information. Our society is not ready for that. Very good things or very bad things can happen equally easily. They will affect all of us. We cannot just watch and count the points.
  • The competitive point of view. Microsoft combines a difficult competitive position with considerable resources: it has both the will and the means to do new things on the scientific, engineering, economical, and social levels. How to resist that? Of course nothing is ever certain…
2010/05/14 15:17
start.txt · Last modified: 2018/08/21 16:35 by leonb

Page Tools