Reminder: WeWork requires that guests check in with an ID. Not sure it will be asked, but safer to take yours.



WeWork message:

Your Meetup will be held at WeWork Wan Chai, 10/F.



Below are some things you should know about hosting a Meetup at WeWork Wan Chai:



- The nearest MTR stop is at Wan Chai, Exit C.

- Our building requires elevator keycard access, but we will ensure the lift is open for your Meetup up during 6-8pm. You may have to place someone at the front entrance of the building and in the WeWork space where your Meetup will take place to open the door.



There are only two things for you to do:



- When your guests arrive at WeWork, they will register under your name.



The summary of HKML Season 1 Episode 1 can be found here: https://gmarti.gitlab.io/hkml/2018/07/18/hkml-s1e1.html



For the Episode 2, I am looking for speakers. If you are interested in presenting something, please contact me with an abstract of your presentation.



We may also introduce a short `asking for help' part where someone presents his machine learning problem, and attendees might propose ideas or solutions.



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Program:



Wilson Fok - Ensemble of convolutional neural networks for heart segmentation



Training an ensemble of convolutional neural networks requires much computational resources for a large set of high-resolution medical 3D scans because deep representation requires many parameters and layers. In this study, 100 3D late gadolinium-enhanced (LGE)-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 0.625 mm from patients with atrial fibrillation were utilized. To contain this training cost, down-sampling of images, transfer learning and ensemble of network’s past weights were deployed. This paper proposes an image processing stage using down-sampling and contrast limited adaptive histogram equalization, a network training stage using a cyclical learning rate schedule, and a testing stage using an ensemble. While this method achieves reasonable segmentation accuracy with the median of the Dice coefficients at 0.87, this method can be used on a computer with a GPU that has a Kepler architecture and at least 3GB memory.



Kris Methajarunon - A summary of ``Machine Learning and Finance: The New Empirical Asset Pricing'' (SoFiE Summer School, Chicago)



Kris will present his takeaways from the SoFiE Summer School at University of Chicago: Machine Learning and Finance: The New Empirical Asset Pricing (https://finmath.uchicago.edu/sites/finmath.uchicago.edu/files/uploads/Stevanovich/Program%20Overview%2C%20Summer%20School%202018%2C%20March%201.pdf)



Gautier Marti - Correlations, Networks and Clustering in Finance



I will present a few techniques used to explore large and/or complex noisy datasets in finance (but which are also applied in biology, chemistry, medicine and social sciences): clusters and networks.