Past Meetup

Unsupervised Learning (Soumith Chintala) & Music Through ML (Brian McFee)

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Agenda

A Path To Unsupervised Learning Through Adversarial Networks - (Soumith Chintala, Researcher at Facebook AI Research)

Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning. He holds a Masters in CS from NYU, and spent time in Yann LeCun's NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.

Soumith will go over generative adversarial networks, a particular way of training neural networks to build high quality generative models. The talk will take you through an easy to follow timeline of the research and improvements in adversarial networks, followed by some future directions, as well as applications.

Understanding Music Through Machine Learning - (Brian McFee, Moore-Sloan Fellow at New York University's Center for Data Science)

Brian McFee is a Moore-Sloan Fellow at New York University's Center for Data Science. He received a B.S. degree in Computer Science from the University of California, Santa Cruz in 2003, and Ph.D. in Computer Science and Engineering from the University of California, San Diego in 2012. His work touches on various topics at the intersection of machine learning, information retrieval, and audio analysis. He is a contributor to various open source projects, and a principal developer of the librosa package for music analysis in Python.

Brian will talk about how we can understand music through machine learning. Music signals share much in common with other high-dimensional data domains (e.g., images or speech), but the domain also imposes unique constraints which can inform the problem formulations and be exploited during the modeling process. In this talk, he will give two examples of recent work on statistical musical analysis: structure analysis and instrument recognition. He will provide pointers to data sets and open source implementations wherever possible.

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