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Thread: Miscellaneous

  1. #11

    Deep Learning - Jurgen Schmidhuber

    Published on Apr 14, 2014

    We're very excited to have one of the world experts in this field at our first meetup. The recent resurrection of multi-layer neural networks is generating a lot of interest currently, with deep learning appearing on the New York Times front page, and big companies like Google and Facebook hunting for the experts in this field. J?rgen's talk will shed more light on how deep learning methods work, and why they work.

  2. #12

    David Silver (Google DeepMind) - Deep Reinforcement Learning

    Published on May 18, 2015

    ICLR 2015 Invited Talk: David Silver (Google DeepMind) "Deep Reinforcement Learning"

  3. #13
    Article "Energy-friendly chip can perform powerful artificial-intelligence tasks"
    Advance could enable mobile devices to implement “neural networks” modeled on the human brain.

    by Larry Hardesty
    February 3, 2016

  4. #14

    Distinguished Lecturer : Eric Xing - Strategies & Principles for Distributed Machine Learning

    Published on Feb 16, 2016

    Eric Xing - Distinguished Lecturer

    Strategies & Principles for Distributed Machine Learning

    The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with 10s to 1000s of machines, it is often the case that significant engineering efforts are required --- and one might fairly ask if such engineering truly falls within the domain of ML research or not. Taking the view that Big ML systems can indeed benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our resent effort on industrial-scale ML solutions that involve a continuum from application, to engineering, and to theoretical research and development of Big ML system and architecture, on how to make them efficient, general, and with convergence and scaling guarantees. These principles concern four key questions which traditionally receive little attention in ML research: How to distribute an ML program over a cluster? How to bridge ML computation with inter-machine communication? How to perform such communication? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typical in traditional computer programs, and by dissecting successful cases of how we harness these principles to design both high-performance distributed ML software and general-purpose ML framework, we present opportunities for ML researchers and practitioners to further shape and grow the area that lies between ML and systems.
    This is joint work with the CMU Petuum Team.

  5. #15

  6. #16

  7. #17

    Machine Learning inside Virtual Worlds

    Published on Mar 15, 2016

    The realistic 3-D graphics in video games can help deep-learning algorithms make sense of the real world.

  8. #18
    Article "Microsoft and Google Want to Let Artificial Intelligence Loose on Our Most Private Data"
    New ways to use machine learning without risking sensitive data could unlock new ideas in industries like health care and finance.

    by Tom Simonite
    April 19, 2016

  9. #19

  10. #20

    Robot learns to push object and identifies patch friction model

    Published on Feb 25, 2016

    ICRA 2016 paper:
    A Convex Polynomial Force-Motion Model for Planar Sliding:
    Identification and Application
    Jiaji Zhou, Robert Paolini, J. Andrew Bagnell and Matthew T. Mason
    "A Convex Polynomial Force-Motion Model for Planar Sliding:
    Identification and Application

    by Jiaji Zhou, Robert Paolini, J. Andrew Bagnell and Matthew T. Mason

    "Teaching robots the physics of sliding and pushing objects"

    by Jiaji Zhou
    June 16, 2016

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