58 citations · 95 across the 8 of their papers we have counts for
8 papers
Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
Biological neural networks learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain el…
From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints
Dimitra Maoutsa
How can we learn the laws underlying the dynamics of stochastic systems when their trajectories are sampled sparsely in time? Existing methods either require temporally resolved hi…
Geometric constraints improve inference of sparsely observed stochastic dynamics
Dimitra Maoutsa
The dynamics of systems of many degrees of freedom evolving on multiple scales are often modeled in terms of stochastic differential equations. Usually the structural form of these…
Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems
Dimitra Maoutsa
Stochastic evolution equations describing the dynamics of systems under the influence of both deterministic and stochastic forces are prevalent in all fields of science. Yet, ident…
Deterministic particle flows for constraining stochastic nonlinear systems
Dimitra Maoutsa, Manfred Opper
Devising optimal interventions for constraining stochastic systems is a challenging endeavour that has to confront the interplay between randomness and nonlinearity. Existing metho…
Deterministic particle flows for constraining SDEs
Dimitra Maoutsa, Manfred Opper
Devising optimal interventions for diffusive systems often requires the solution of the Hamilton-Jacobi-Bellman (HJB) equation, a nonlinear backward partial differential equation (…
Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation
Dimitra Maoutsa, Sebastian Reich, Manfred Opper
Fokker-Planck equations are extensively employed in various scientific fields as they characterise the behaviour of stochastic systems at the level of probability density functions…
Inferring network connectivity from event timing patterns
Jose Casadiego, Dimitra Maoutsa, Marc Timme
Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution although these are often experiment…