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Updated: Feb 8, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
Published on: July 15, 2014
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer1, Frank Noé1
1Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany.
Abstract:
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes-beyond the capabilities of linear dimension reduction techniques.
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