{"id":5420,"date":"2020-12-04T16:34:42","date_gmt":"2020-12-04T21:34:42","guid":{"rendered":"https:\/\/blogs.mathworks.com\/deep-learning\/?p=5420"},"modified":"2024-07-02T10:16:05","modified_gmt":"2024-07-02T14:16:05","slug":"mathworks-at-neurips-2020","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/deep-learning\/2020\/12\/04\/mathworks-at-neurips-2020\/","title":{"rendered":"MathWorks at NeurIPS 2020"},"content":{"rendered":"Post by Shounak Mitra<\/a>, Product Manager \u2013 Deep Learning and AI\r\n
<\/h6>\r\nWe are very close to the start of the biggest AI conference of the year! Last year when I left Vancouver after attending NeurIPS 2019, I had marked a few restaurants to try out in 2020. Little did we all know the unprecedented times we all will have to face.\r\n
<\/h6>\r\nMathWorks is proud be a sponsor at the conference and this year we are doing a bunch of activities at the virtual booth, meetups, socials, and workshops. I am writing this blog to share what all MathWorks is doing at NeurIPS 2020, and encourage you to join us if you can!\r\n
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Virtual Booth<\/h2>\r\n

<\/h6>\r\nIf you have used or are currently using MATLAB or Simulink, or looking for your next job opportunity, come say a \u2018hi\u2019 to us at the MathWorks Virtual Booth<\/a>. We will have a team of developers, hiring managers, and product managers who\u2019ll be more than happy to chat with you.\r\n
<\/h6>\r\nTalks at the booth:<\/em> We are arranging 11 Talks at our booth this year. The talks will be on a variety of topics, ranging from deploying AI to embedded devices to how researchers at UMASS Amherst have used deep learning to mimic physics behind finite element modeling. There will also be booth talks on other interesting topics such as LiDAR, how MATLAB works with TensorFlow and PyTorch, AI workflows for Radar, Signal, Time series and Reinforcement learning. For a detailed agenda, please see the full agenda<\/u><\/a><\/strong>.\r\n
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Tuesday \u2013 Dec 8<\/span><\/h3>\r\n<\/td>\r\n

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Wednesday \u2013 Dec 9<\/span><\/h3>\r\n<\/td>\r\n

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Thursday \u2013 Dec 10<\/span><\/h3>\r\n<\/td>\r\n<\/tr>\r\n

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<\/h6>\r\n2:10 - 2:30 PM<\/strong><\/td>\r\n
AI System Design - <\/strong>Simulation and Code Generation\"\"\r\n\r\nBill Chou & Emmanouil, Product Managers\r\n
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Application of GANs<\/strong> to Improve Characterization of Porous Media Microstructure and Fabric\r\n\r\n\"\"\r\n
<\/h6>\r\nTim Anderson, Stanford<\/td>\r\n
AI for Time-series and Signal Applications <\/strong>\r\n\r\n\"\"\r\n\r\n \r\n\r\nGabrielle Bunkheila & Esha Shah Product Managers<\/td>\r\n<\/tr>\r\n
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<\/h6>\r\n2:30-2:50 PM<\/strong><\/td>\r\n
AI System Design - <\/strong>Simulation and Code Generation (Continued)\r\n
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On the Effectiveness of Bayesian AutoML methods for Physics Emulators<\/strong>\r\n
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<\/h6>\r\nPeetak Mitra, UMASS<\/td>\r\n
Reinforcement Learning with MATLAB and Simulink<\/strong>\r\n
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<\/h6>\r\nEmmanouil, Product Manager<\/td>\r\n<\/tr>\r\n
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<\/h6>\r\n7:10-7:30 PM<\/strong><\/td>\r\n
Lidar Perception<\/strong>: Semantic Segmentation and Object Detection on 3D Point Clouds\r\n
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<\/h6>\r\nAvi Nehemiah, Product Manager<\/td>\r\n
AI workflows for Radar and Communications<\/strong>\r\n
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<\/h6>\r\nRick Gentile, Product Manager<\/td>\r\n
AI Workflows in the Cloud<\/strong>\r\n
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<\/h6>\r\nDavid Willingham, Product Manager<\/td>\r\n<\/tr>\r\n
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<\/h6>\r\n7:30-7:50 PM<\/strong><\/td>\r\n
Evolution of Deep Learning with MATLAB<\/strong>\"\"\r\n\r\nDavid Willingham, Product Manager<\/td>\r\nHow MATLAB works with TensorFlow and PyTorch\r\n\r\n\"\"\r\n\r\nShounak Mitra, Product Manager<\/td>\r\nQuantizing Deep Learning Networks <\/strong>for GPUs and FPGAs with MATLAB\r\n\r\n\"\"\r\n\r\nGreg Coppenrath, Product Manager<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
<\/h6>\r\nHiring at the booth:<\/em> The booth will also have private chats scheduled with our recruiters and hiring managers. To explore a list of opportunities, please go to Careers at MathWorks<\/a>.\r\n
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Paper at NeurIPS<\/h2>\r\nThis year we have a paper at NeurIPS: On the Effectiveness of Bayesian AutoML methods for Physics Emulators.<\/u> The authors of this paper will also give a talk at the conference. In this work, a data driven emulator for turbulence closure terms in the context of Large Eddy Simulation (LES) models is trained using Artificial Neural Networks and an autoML frame- work based on Bayesian Optimization, incorporating priors to jointly optimize the hyper-parameters as well as conduct a full neural network architecture search to converge to a global minima, is proposed.\r\n
<\/h6>\r\nTo watch the recording of the talk, please click here<\/a>.\r\n
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NeurIPS 2020 Socials<\/strong><\/h2>\r\n
<\/h6>\r\nWe are organizing three socials at NeurIPS 2020, and two at WiML (Women in Machine Learning) workshop co-located with NeurIPS.\r\n
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Title<\/strong><\/td>\r\nWhen<\/strong><\/td>\r\nContact<\/strong><\/td>\r\n\r\n<\/tr>\r\n
Equity and Ethics in AI from the Perspective of Black Women in AI<\/td>\r\nTuesday December 8, 5-7pm EST\r\nWednesday December 9, 8-9am EST (WiML)<\/td>\r\nLouvere Walker-Hannon<\/td>\r\n\r\n<\/tr>\r\n
Un-Bookclub Race After Technology<\/td>\r\nWednesday December 9, 8-9am (WiML)\r\nWednesday December 9, 5-7pm EST<\/td>\r\nAnoush Najarian<\/td>\r\n\r\n<\/tr>\r\n
Women in AI Ignite<\/td>\r\nThursday December 10, 3-5pm EST<\/td>\r\nAnoush Najarian<\/td>\r\n\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
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NeurIPS 2020 Meetups<\/strong><\/h3>\r\n
<\/h6>\r\nThe goal of NeurIPS meetups is to open up access to communities worldwide and to connect participants by geographic area and language. With this, NeurIPS hopes to support the growth of AI expertise around the world, including in underrepresented communities in tech, and to fuel innovation responsibly.\r\n
<\/h6>\r\nWe are excited to have two MathWorkers on the NeurIPS organizing committee: Louvere Walker-Hannon and Anoush Najarian are NeurIPS meetup co-chairs, working with Emtiyaz Khan of the Riken Center for AI in Tokyo.\r\n
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