Page 5 of 5 FirstFirst ... 345
Results 41 to 50 of 50

Thread: Miscellaneous

  1. #41


    Deep reinforcement learning for driving policy

    Published on Jan 31, 2017

    Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways.

    Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy.

    Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too defensive so that normal traffic flow is maintained.

    Symposium on "Information, Control, and Learning" at The Hebrew University of Jerusalem.

    By Prof. Shai Shalev Shwartz, VP Technologies of Mobileye
    and professor of computer science at The Hebrew University of Jerusalem.
    Mobileye N.V., Jerusalem, Israel

  2. #42

  3. #43


    Reinforcement learning to quadrotor control

    Published on Mar 3, 2017

  4. #44


    On Deep Learning with Ian Goodfellow, Andrew Trask, Kelvin Lwin, Siraj Raval and the Udacity Team

    Streamed live on Mar 17, 2017

    Join us on March 17 at 6pm PST for a panel on the state of deep learning. Brought to you by Udacity's Deep Learning Nanodegree Foundation program.

  5. #45
    Article "The Dark Secret at the Heart of AI"
    No one really knows how the most advanced algorithms do what they do. That could be a problem.

    by Will Knight
    April 11, 2017

  6. #46


    Teaching robots to teach robots

    Published on May 10, 2017

  7. #47


    Forget catastrophic forgetting: AI that learns after deployment

    Published on May 16, 2017

    Neurala CTO Anatoly Gorshechnikov on Lifelong Deep Learning Neural Networks. One of the major hassles of Deep Learning is the need to fully retrain the network on server every time new data becomes available in order to preserve the previous knowledge. This is called 'catastrophic forgetting' and severely impairs the ability to develop a truly autonomous AI. We present the patent pending technology that allows us to solve this problem by simply training on the fly the new object without retraining of the old. Our results not only show state of the art accuracy, but real time performance suitable for deployment of AI directly on the edge, thus moving AI out of the server room and into the hands of consumers. Imagine a toy that can learn to recognize and react to its owner or a drone that can learn and detect objects of interest identified while in flight. (Recorded at the NVIDIA GTC Conference in 2017 at San Jose.)

  8. #48


    The Deep End of Deep Learning | Hugo Larochelle | TEDxBoston

    Published on Oct 12, 2016

    Artificial Neural Networks are inspired by some of the "computations" that occur in human brains—real neural networks. In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media. Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning.

    Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS). Before 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017.

  9. #49


    Stanford Seminar - Crowdsourcing for machine learning

    Published on Jun 6, 2017

    CS547: Human-Computer Interaction Seminar
    Crowdsourcing for Machine Learning
    Speaker: Dan Weld, University of Washington

  10. #50
    Article "'Black box' technique may lead to more powerful AI"
    The strategy is easier, faster and more flexible.

    by Jon Fingas
    March 26, 2017

Page 5 of 5 FirstFirst ... 345

Социальные закладки

Социальные закладки

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts
  •