358 citations · 984 across the 26 of their papers we have counts for
3 papers · 1 filter
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions. Our method estimates an effective probability distr…
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of…
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
Siddharth Siddharth, Tzyy-Ping Jung, Terrence J. Sejnowski
In recent years, the use of bio-sensing signals such as electroencephalogram (EEG), electrocardiogram (ECG), etc. have garnered interest towards applications in affective computing…