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Found 4 papers, 1 papers with code
Learning interpretable continuous-time models of latent stochastic dynamical systems
We develop an approach to learn an interpretable semi-parametric model of a latent continuous-time stochastic dynamical system, assuming noisy high-dimensional outputs sampled at uneven times.
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
Empirical fixed point bifurcation analysis
In a common experimental setting, the behaviour of a noisy dynamical system is monitored in response to manipulations of one or more control parameters.
Unlocking neural population non-stationarities using hierarchical dynamics models
Neural population activity often exhibits rich variability.
