Call for Abstracts
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BayLearn 2014: Bay Area Machine Learning Symposium
Meet the Bay Area Machine Learning Scientists on October 21, 2014!
Feel free to circulate this invitation to your colleagues and relevant contacts.
** Call for Abstract and Invitation **
The BayLearn Symposium is an annual gathering of machine learning scientists from the San Francisco Bay Area. While it promotes community building between local researchers from academic and industrial institutions, it also welcomes visitors. This one-day event combines invited talks, contributed talks and posters to foster exchange of ideas. This year, we’re very happy to have four great invited speakers: Geoff Hinton, Chris Manning, Kevin Murphy and Ben Recht.
http://www.baylearn.org/schedule
** Dates **
September 14th, 2014 - Abstract submission deadline
October 3rd, 2014 - Author notification
October 9th, 2014 - Registration Deadline
October 21st, 2014 - Symposium
** Venue **
The event will be hosted at the
Berkeley Art Museum.
Berkeley, CA
** Submissions **
We encourage submission of abstracts. Acceptable material includes work which has already been submitted or published, preliminary results and controversial findings. We do not intend to publish paper proceedings, only abstracts will be shared through an online repository. Our primary goal is to foster discussion!
Please submit the abstracts as a 2-page pdf in the NIPS format
http://www.baylearn.org/submissions
** Registration**
The event is free! Registration is open. We suggest that you register quickly as seating is limited. However, please refrain from registering if you do not really intend on coming, or unregister if you know you will not be able to make it. Registration deadline is October 09, 2014.
http://www.baylearn.org/registration
** Event Sponsors **
AT&T
Bosch
Criteo
Qualcomm
UC Berkeley
** Organizing Committee **
- Samy Bengio, Google Research
- Joaquin Quinonero Candela, Facebook
- Olivier Chapelle, Criteo
- Jean-Francois Paiement, AT&T
- Alexei Pozdnukhov, UC Berkeley
- Mohak Shah, Bosch
Questions? Please email
us at in...@baylearn.org
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