BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Columbia Entrepreneurship - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://entrepreneurship.columbia.edu
X-WR-CALDESC:Events for Columbia Entrepreneurship
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20160101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20170905T110000
DTEND;TZID=UTC:20170905T120000
DTSTAMP:20260510T182834
CREATED:20170905T134650Z
LAST-MODIFIED:20170905T135252Z
UID:10202-1504609200-1504612800@entrepreneurship.columbia.edu
SUMMARY:Data Science Institute Colloquium: Kevin Murphy | Research Scientist\, Google (Keynote)
DESCRIPTION:OPEN TO ALL – NO REGISTRATION REQUIRED \n\n\nTowards Machines that Perceive and Communicate \nABSTRACT: I will summarize some recent work related to visual scene understanding and “grounded” language understanding. In particular\, I will discuss a connected group of results from my group at Google: \n– Our DeepLab system for semantic segmentation (PAMI’17) [1].\n– Our object detection system (CVPR ‘17 and 1st place in COCO’16) [2].\n– Our instance segmentation system (2nd place in COCO’16)\n– Our person detection/pose estimation system [3] (CVPR’17 and 2nd place in COCO’16)\n– Visually grounded referring expressions (CVPR’16) [4].\n– Discriminative image captioning (CVPR’17) [5].\n– Optimizing semantic metrics for image captioning using RL (ICCV’17) [6]\n– Generative models of visual imagination (submitted to NIPS’17). \nI will explain how each of these pieces can be combined to develop systems that can better understand images and words. \nBIO: Bio: Kevin Murphy is a research scientist at Google in Mountain View\, California\, where he works on AI\, machine learning\, and computer vision. Before joining Google in 2011\, he was an associate professor (with tenure) of computer science and statistics at the University of British Columbia in Vancouver\, Canada. Before starting at UBC in 2004\, he was a postdoc at MIT. Kevin got his BA from U. Cambridge\, his MEng from U. Pennsylvania\, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals\, as well as an 1100-page textbook called “Machine Learning: a Probabilistic Perspective” (MIT Press\, 2012)\, which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research). \n\n\nEvent Contact Information: \nData Science Institute\n212-854-5660\ndatascience@columbia.edu
URL:https://entrepreneurship.columbia.edu/event/data-science-institute-colloquium-kevin-murphy-research-scientist-google/
CATEGORIES:Columbia Data Science Institute
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170920T163000
DTEND;TZID=UTC:20170920T180000
DTSTAMP:20260510T182834
CREATED:20170915T152633Z
LAST-MODIFIED:20170915T152633Z
UID:10409-1505925000-1505930400@entrepreneurship.columbia.edu
SUMMARY:Data\, Ethics and Decision-Making Lecture Series: Dr. Jeanette M. Wing\, Avanessians Director of the Data Science Institute
DESCRIPTION:OPEN TO ALL \nABSTRACT: \nThe Data\, Ethics\, and Decision-making Speaker Series hosted by the Institute for Social and Economic Research and Policy (ISERP) presents Dr. Jeannette M. Wing\, Avanessians Director of the Data Science Institute and Professor of Computer Science in at Columbia University\, on “Using Data for Good: What does it mean?“. Professor Wing will be laying out her vision for the Institute\, which will focus on her desire to think seriously about the definition and practice of using data for good.
URL:https://entrepreneurship.columbia.edu/event/data-ethics-decision-making-lecture-series-dr-jeanette-m-wing-avanessians-director-data-science-institute/
CATEGORIES:Columbia Data Science Institute
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170922T100000
DTEND;TZID=UTC:20170922T110000
DTSTAMP:20260510T182834
CREATED:20170915T152924Z
LAST-MODIFIED:20170915T152924Z
UID:10412-1506074400-1506078000@entrepreneurship.columbia.edu
SUMMARY:Data Science Institute Colloquium Series Event: What Can Deep Learning Learn from Linear Regression
DESCRIPTION:OPEN TO ALL \nABSTRACT \nWhen training large-scale deep neural networks for pattern recognition\, hundreds of hours on clusters of GPUs are required to achieve state-of-the-art performance. Improved optimization algorithms could potentially enable faster industrial prototyping and make training contemporary models more accessible. \nIn this talk\, I will attempt to distill the key difficulties in optimizing large\, deep neural networks for pattern recognition. In particular\, I will emphasize that many of the popularized notions of what make these problems “hard” are not true impediments at all. I will show that it is not only easy to globally optimize neural networks\, but that such global optimization remains easy when fitting completely random data. \nI will argue instead that the source of difficulty in deep learning is a lack of understanding of generalization—namely understanding behavior on new and unseen data. By appealing to standard concepts from linear regression\, I will describe why certain popular theories of generalization fail to explain the success of large neural nets. I will close with some possible approaches to patching this theory and guiding the engineering of deep learning models with enormous capacity. \n 
URL:https://entrepreneurship.columbia.edu/event/data-science-institute-colloquium-series-event-can-deep-learning-learn-linear-regression/
CATEGORIES:Columbia Data Science Institute
END:VEVENT
END:VCALENDAR