dida regards itself as a company at the interface of scientific research and the practical application of Machine Learning, with scientists from well-known international research institutions.
Here we list academic papers that our Machine Learning Scientists have written or contributed to.
|2020||Algebraic topology||A framework for geometric field theories and their classification in dimension one||(submitted)|
|2020||Nonparametric Statistics||Kernel Conditional Density Operators||AISTATS 2020|
|2020||Stochastic processes||Model Order Reduction for (Stochastic-) Delay Equations With Error Bounds||(submitted)|
|2020||Statistics, Machine Learning Theory||VarGrad: A Low-Variance Gradient Estimator for Variational Inference||Advances in Neural Information Processing Systems|
|2020||Stochastic processes, Optimal Control, PDEs||Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space||(submitted)|
|2020||Machine Learning Theory||Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces||Advances in Dynamics, Optimization and Computation (book chapter)|
|2020||Nonparametric Statistics||On data-driven choice of 𝜆 in nonparametric Gaussian regression via Propagation–Separation approach||Computational Statistics & Data Analysis|
|2020||Stochastic processes||Kernel autocovariance operators of stationary processes: Estimation and convergence||(submitted)|
|2019||Applied topology||The Conley index for discrete dynamical systems and the mapping torus||Journal of Applied and Computational Topology|
|2019||Quantum physics||Quantum rolling friction||Physical review letters|
|2019||Nonlinear dynamics, Machine Learning Theory||Kernel methods for detecting coherent structures in dynamical data||Chaos: An Interdisciplinary Journal of Nonlinear Science|
|2019||Statistical physics||Variational approach to rare event simulation using least-squares regression||Chaos|
|2019||Stochastic processes||Feedback control theory & Model order reduction for stochastic equations||(submitted)|
|2019||Quantum physics||Extended hydrodynamic description for nonequilibrium atom-surface interactions||JOSA B|
|2018||Theoretical physics||Holographic Gauged NJL Model: the Conformal Window and Ideal Walking||Physical Review D|
|2018||Quantum physics||Nonequilibrium atom-surface interaction with lossy multilayer structures||Physical Review A|
|2018||Algebraic topology||Supersymmetric field theories from twisted vector bundles||Communications in Mathematical Physics|
|2018||Algebraic geometry, High energy physics||Toric geometry and regularization of Feynman integrals||(unpublished)|
|2017||Theoretical physics||A Holographic Study of the Gauged NJL Model||Physical Letters B|
|2017||Statistical physics||Variational characterization of free energy: Theory and algorithms||Entropy|
|2017||Theoretical physics||Holograms of a Dynamical Top Quark||Physical Review D|
|2017||Algebraic topology||Dimensional reduction and the equivariant Chern character||Algebraic and Geometric Topology|
|2016||Nonlinear dynamics||Onset of time dependence in ensembles of excitable elements with global repulsive coupling||Physical Review E|
|2016||Computational Dynamical Systems||Discretization strategies for computing Conley indices and Morse decompositions of flows||Journal of Computational Dynamics|
|2016||Computational Neuroscience||Mechanisms of self-sustained oscillatory states in hierarchical modular networks with mixtures of electrophysiological cell types||Frontiers in Computational Neuroscience|
|2015||Computational Dynamical Systems||A topological approach to the algorithmic computation of the Conley index for Poincaré maps||SIAM Journal on Applied Dynamical Systems|
|2014||Computational Neuroscience||Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types||Frontiers in Computational Neuroscience|
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