Recurrent neural networks like long short-term memory (LSTM) have been utilized  as a tool for modeling and predicting dynamics of complex stochastic molecular systems. Previous studies have shown that Transformer has an advantage over LSTM in dealing with the memory loss of long-sequence data, and exceeds LSTM in many natural language processing tasks. In this seminar, we will show the implementation of Transformer on learning molecular dynamics and compare it with LSTM, which is greatly affected by lag time. 

3 May 2021
3:00pm - 4:00pm
Where
https://hkust.zoom.com.cn/j/6218914432 (Passcode: hkust)
Speakers/Performers
Miss Wenqi ZENG
Organizer(S)
Department of Mathematics
Contact/Enquiries
Payment Details
Audience
Alumni, Faculty and staff, PG students, UG students
Language(s)
English
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