Last Week, Lake Tahoe, California was host to the Neural Information Processing Systems (NIPS) conference, co-hosted by Google. Over 300 papers were accepted and presented in a combination of oral presentations, poster sessions, and demonstrations. Below are just a few highlights of the latest research on display at #nips2013 :
During Friday’s morning spotlight session, Google Software Engineer Krzysztof Choromanski and co-authors Tony Jebara and Kui Tang of Columbia University presented Adaptive Anonymity via b-Matching. In it, the authors explored anonymity settings for user data that would allow maximal utility for machine learning applications while preserving an individual’s level of desired privacy. This work contributes novel efficient algorithms that are straightforward to implement and that secures user data to respect each individual’s privacy settings. Read the full paper at http://goo.gl/KDccsC.
On Saturday, joint postdoctoral fellow at the Courant Institute of Mathematical Sciences and Memorial Sloan-Kettering Cancer Center Marius Kloft presented Learning Kernels Using Local Rademacher Complexity, co-authored by and of Google Research. In the paper, the authors seek to use a finer notion of complexity for the design of Kernel-based algorithms used in Machine Learning (ML) by utilizing a local Rademacher complexity, which takes into consideration that the hypotheses selected by a learning algorithm have better performance and belong to a favorable sub-family of the set of all hypotheses. Learn more at http://goo.gl/r1ibkQ.
In addition to the talks, NIPS featured demos from industry and academia alike, allowing conference participants a glimpse at the latest research developments in robotics, machine learning, music recommendation, and more (see photos below). During the demo session, Googlers Tomas Mikolov, Kai Chen, and showcased Distributed Representations of Words and Phrases and their Compositionality, a demonstration of word and phrase representations derived from neural network models that were trained on about one hundred billion words.
Attendees were able to take part in interactive applications where they were able to search for similar phrases, search queries and sentences, visualize word vectors, and get answers to analogies they supplied. To learn more about the work behind this demo, visit our recent post at http://goo.gl/Zymgcg
To access all of the papers presented at NIPS 2013, including all reviews and author rebuttals, visit http://goo.gl/bgiEky
Saw this during my recent visit to Woods Hole Oceanographic Institute.
- McGill UniversityPhD, Computer Science, 2007 - present
- Rensselaer Polytechnic InstituteBS, MS, Computer Science, 1999 - 2005
- KitwareR&D, 2005 - 2006
- Open Box TechEngineer, 2006 - 2007
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