Virtual BoothIf you have used or are currently using MATLAB or Simulink, or looking for your next job opportunity, come say a ‘hi’ to us at the MathWorks Virtual Booth. We will have a team of developers, hiring managers, and product managers who’ll be more than happy to chat with you. Talks at the booth: 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.
Tuesday – Dec 8
Wednesday – Dec 9
Thursday – Dec 10
|2:10 - 2:30 PM||AI System Design - Simulation and Code Generation Bill Chou & Emmanouil, Product Managers||Application of GANs to Improve Characterization of Porous Media Microstructure and Fabric Tim Anderson, Stanford||AI for Time-series and Signal Applications Gabrielle Bunkheila & Esha Shah Product Managers|
|2:30-2:50 PM||AI System Design - Simulation and Code Generation (Continued)||On the Effectiveness of Bayesian AutoML methods for Physics Emulators Peetak Mitra, UMASS||Reinforcement Learning with MATLAB and Simulink Emmanouil, Product Manager|
|7:10-7:30 PM||Lidar Perception: Semantic Segmentation and Object Detection on 3D Point Clouds Avi Nehemiah, Product Manager||AI workflows for Radar and Communications Rick Gentile, Product Manager||AI Workflows in the Cloud David Willingham, Product Manager|
|7:30-7:50 PM||Evolution of Deep Learning with MATLAB David Willingham, Product Manager||How MATLAB works with TensorFlow and PyTorch Shounak Mitra, Product Manager||Quantizing Deep Learning Networks for GPUs and FPGAs with MATLAB Greg Coppenrath, Product Manager|
Paper at NeurIPSThis year we have a paper at NeurIPS: On the Effectiveness of Bayesian AutoML methods for Physics Emulators. 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. To watch the recording of the talk, please click here.
NeurIPS 2020 SocialsWe are organizing three socials at NeurIPS 2020, and two at WiML (Women in Machine Learning) workshop co-located with NeurIPS.
|Equity and Ethics in AI from the Perspective of Black Women in AI||Tuesday December 8, 5-7pm EST Wednesday December 9, 8-9am EST (WiML)||Louvere Walker-Hannon||sign-up|
|Un-Bookclub Race After Technology||Wednesday December 9, 8-9am (WiML) Wednesday December 9, 5-7pm EST||Anoush Najarian||sign-up|
|Women in AI Ignite||Thursday December 10, 3-5pm EST||Anoush Najarian||sign-up|
NeurIPS 2020 MeetupsThe 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. We 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.
- Blog post: https://neuripsconf.medium.com/neuips-meetup-update-f07986c97c2b
- Call for meetups https://neurips.cc/Conferences/2020/CallForMeetups
- Boston, US – viewing of Coded Bias documentary Friday December 11, 11am EST, and multiple sessions throughout the week
- Cambridge, UK and Munich, Germany – December 9, noon-2 EST
- Bangalore and Hyderabad, India - December 18, 3-5pm IST (Indian Standard Time)
- Tokyo, Japan - December 6, 9 & 12, in collaboration with RIKEN Center for AI
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