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Research#AI Testing📝 BlogAnalyzed: Dec 29, 2025 08:31

A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100

Published:Jan 24, 2018 17:08
1 min read
Practical AI

Analysis

This article summarizes a podcast episode discussing the 2017 NIPS Best Paper Award winner, "A Linear-Time Kernel Goodness-of-Fit Test." The podcast features interviews with the paper's authors, including Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo, and Kenji Fukumizu. The discussion covers the concept of a "goodness of fit" test and its application in evaluating statistical models against real-world scenarios. The episode also touches upon the specific test presented in the paper, its practical applications, and its relationship to the authors' other research. The article also includes a promotional announcement for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco.
Reference

“In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario.”

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Research#AI in Music📝 BlogAnalyzed: Dec 29, 2025 08:32

Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98

Published:Jan 19, 2018 16:07
1 min read
Practical AI

Analysis

This article discusses a podcast episode featuring Eric Humphrey, a research scientist at Spotify, focusing on separating vocals from recorded music using deep learning. The conversation covers Spotify's use of its vast music catalog for training algorithms, the application of architectures like U-Net and Pix2Pix, and the concept of "creative AI." The article also promotes the upcoming RE•WORK Deep Learning Summit in San Francisco, highlighting key speakers and offering a discount code. The core focus is on the technical aspects of music understanding and AI's role in it, specifically within the context of Spotify's research.
Reference

“We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms.”

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Research#deep learning📝 BlogAnalyzed: Dec 29, 2025 08:32

Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97

Published:Jan 17, 2018 22:19
1 min read
Practical AI

Analysis

This article discusses an interview with Greg Diamos, a senior computer systems researcher at Baidu, focusing on accelerating deep learning training. The core topic revolves around using mixed 16-bit and 32-bit floating-point arithmetic to improve efficiency. The conversation touches upon systems-level thinking for scaling and accelerating deep learning. The article also promotes the RE•WORK Deep Learning Summit, highlighting upcoming events and speakers. It provides a discount code for registration, indicating a promotional aspect alongside the technical discussion. The focus is on practical applications and advancements in AI chip technology.
Reference

“Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic.”

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