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    Machine Learning for Signal Processing Applications with Stuart Feffer & Brady Tsai
    800 800 This Week in Machine Learning & AI

    In this episode, I’m joined by Stuart Feffer, co-founder and CEO of Reality AI, which provides tools and services for engineers working with sensors and signals, and Brady Tsai, Business Development Manager at Koito, which develops automotive lighting solutions for car manufacturers.

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    Personalizing the Ferrari Challenge Experience with Andy Keller and Emile Chin-Dickey
    800 800 This Week in Machine Learning & AI

    In this episode, I’m joined by Andy Keller and Emile Chin-Dickey to discuss Intel’s partnership with the Ferrari Challenge North American Series.

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    Deep Learning for 3D Sensors and Cameras in Lighthouse with Alex Teichman
    800 800 This Week in Machine Learning & AI

    In this episode, I sit down with Alex Teichman, CEO and Co-Founder of Lighthouse, a company taking a new approach to the in-home smart camera.

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    Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein
    800 800 This Week in Machine Learning & AI

    In this episode, I’m joined by Andrew Stein, computer vision engineer at consumer robotics company Anki, and his partner in crime Cozmo, a toy robot with tons of personality.

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    Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! with Inmar Givoni
    800 800 This Week in Machine Learning & AI

    In this episode i’m joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach.

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    A Linear-Time Kernel Goodness-of-Fit Test with Wittawat Jitkrittum, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton – NIPS Best Paper ’17
    800 800 This Week in Machine Learning & AI

    In this episode, Sam Charrington is joined by the authors of “A Linear-Time Kernel Goodness-of-Fit Test,” a NIPS ’17 Best Paper Award Winner.

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    Safe and Nested Subgame Solving for Imperfect-Information Games with Tuomas Sandholm – NIPS Best Paper ’17
    800 800 This Week in Machine Learning & AI

    In this episode, I speak with Tuomas Sandholm, Carnegie Mellon University Professor and, along with his PhD student Noam Brown, winner of a 2017 NIPS Best Paper award for the paper “Safe and Nested Subgame Solving for Imperfect-Information Games.”

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    Separating Vocals in Recorded Music at Spotify with Eric Humphrey
    800 800 This Week in Machine Learning & AI

    In today’s show, I sit down with Eric Humphrey, Research Scientist in the music understanding group at Spotify. Eric was at the Deep Learning Summit to give a talk on Advances in Deep Architectures and Methods for Separating Vocals in Recorded Music.

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    Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos
    800 800 This Week in Machine Learning & AI

    In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.”

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    Composing Graphical Models With Neural Networks with David Duvenaud
    800 800 This Week in Machine Learning & AI

    In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning Summit on “Composing Graphical Models With Neural Networks for Structured Representations and Fast Inference.”

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    Embedded Deep Learning at Deep Vision with Siddha Ganju
    800 800 This Week in Machine Learning & AI

    In this episode we hear from Siddha Ganju, data scientist at computer vision startup Deep Vision. Siddha joined me at the AI Conference a while back to chat about the challenges of developing deep learning applications “at the edge,” i.e. those targeting compute- and power-constrained environments.

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    Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley
    800 800 This Week in Machine Learning & AI

    Today, I’m joined by Kenneth Stanley, Professor in the Department of Computer Science at the University of Central Florida and senior research scientist at Uber AI Labs.

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