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. 

5月3日
3:00pm - 4:00pm
地點
https://hkust.zoom.com.cn/j/6218914432 (Passcode: hkust)
講者/表演者
Miss Wenqi ZENG
主辦單位
Department of Mathematics
聯絡方法
付款詳情
對象
Alumni, Faculty and staff, PG students, UG students
語言
英語
其他活動
3月24日
研討會, 演講, 講座
IAS / School of Science Joint Lecture - Pushing the Limit of Nonlinear Vibrational Spectroscopy for Molecular Surfaces/Interfaces Studies
Abstract Surfaces and interfaces are ubiquitous in Nature. Sum-frequency generation vibrational spectroscopy (SFG-VS) is a powerful surface/interface selective and sub-monolayer sensitive spect...
11月22日
研討會, 演講, 講座
IAS / School of Science Joint Lecture - Leveraging Protein Dynamics Memory with Machine Learning to Advance Drug Design: From Antibiotics to Targeted Protein Degradation
Abstract Protein dynamics are fundamental to protein function and encode complex biomolecular mechanisms. Although Markov state models have made it possible to capture long-timescale protein co...