Hello He Wang, The Apache MXNet team has accepted your talk. We are very excited to have you present on this event. Your talk has been scheduled at 10:01 AM PST on Dec 14th for 15 minutes.
The agenda/time is still evolving. We will inform you if there are any changes. Here is the updated website - https://s.apache.org/MXNet2020 Action Required: * We request you to send your recorded talk (15 minutes) to us by Dec 6th midnight PST. * Please respond by end of tomorrow to confirm your presentation and attendance. Question Answers: We plan to have a slack channel in parallel to your recording where you can respond to questions while your recording plays. We will add the slack channel names to the respective talks in a week. Please reach out if you have any questions. We will send out the webcast details by end of Wednesday. Thank you Vartika From: 王赫 <[email protected]> Date: Sunday, November 8, 2020 at 11:03 PM To: apachemxnetday <[email protected]> Subject: Re: Presentation Requested for Apache MXNet Day I would like to give a presentation on my recent work. It relates on category of "research and applications" for Apache MXNet. Title: Matched-filtering Techniques and Deep Neural Networks —— Application for Gravitational Wave Astronomy Abstract: Deep learning is a neural-inspired pattern recognition technique that has been shown to be as effective as conventional signal processing. And It has been shown have considerable potential to identify gravitational-wave (GW) signals in highly noisy data. In this talk, I will first review some related works on the detection and characterization of GW signals and some fundamental background of GW data. I will then present our recent paper (DOI: 10.1103/physrevd.101.104003<https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.104003>) about the effect of matched-filtering convolutional neural networks (MFCNN) we proposed on the GW recognition and identifying generalization properties of gravitational waves. Powered by MXNet, a brand-new network architecture is presented. At last, some insights on the model are presented. He Wang ------------------------------------------------------------- He WANG (王赫), Postdoc at ITP-CAS, Ph.D. from BNU. Member of KAGRA collaboration. Phone: +86 188 1155 7200 Email: [email protected]<mailto:[email protected]>/ [email protected]<mailto:[email protected]> My Site: https://iphysresearch.github.io/ -------------------------------------------------------------
