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Learning Itô Diffusions from Time Series
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Machine Learning and Dynamical Systems
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- 1 Machine Learning for Prediction of Terrestial Climate and Weather
- 2 Transport in Reservoir Computing
- 3 Statistics of Attractor Embeddings in Reservoir Computing
- 4 Time Shifts to Reduce the Size of Reservoir Computers
- 5 Universal Approximation Thms for Continuous Functions of Càdlàg Paths & Lévy-Type Signature Models
- 6 A Representation Theoretic View on Signature Transforms
- 7 On Explaining the Surprising Success of Reservoir Computing Forecaster of Chaos and Other Random...
- 8 Koopman Operator Theory Based Machine Learning of Dynamical Systems
- 9 Using Statistical Mechanics to Approach the Optimal Size of a Network in Image Recognition
- 10 Residual Dynamic Mode Decomposition: Rigorous Data-Driven Computation of Spectral Properties...
- 11 Estimation of Interactions among Dynamical Elements by Koopman Operator
- 12 Learning Itô Diffusions from Time Series
- 13 Emergent Hypernetworks in Oscillator Networks
- 14 Learning and Forecasting the Effective Dynamics of Complex Systems across Scales
- 15 Learning Emergent PDEs in Learned Emergent Spaces
- 16 Learning Reversible Symplectic Dynamics
- 17 Combinatorial Topological Dynamics
- 18 Data-Driven Reduced Order Models Using Invariant Foliations, Manifolds and Autoencoders
- 19 Thoughts on the Future of Governing Equations
- 20 A Coarse-Graining Approach to Mapping Cortical Parameter Space
- 21 Optimal Transport for Learning Chaotic Dynamics via Invariant Measures
- 22 Diffrax: Numerical Differential Equation Solvers in JAX
- 23 Dissipative Deep Neural Dynamical Systems
- 24 Deep Learning for Nonlinear Stability Analysis in Dynamical Systems
- 25 Active Learning in Efficient Estimate for Basin Stability of Dynamic Networks
- 26 Predicting the Impact of Treatment over Time with Uncertainty Aware Neural Differential Equations
- 27 Approximation Theory of Deep Learning from the Dynamical Viewpoint
- 28 r-Adaptivity, Deep Learning and the Deep Ritz Method
- 29 A Proximal Method for Sampling
- 30 Momentum Stiefel Optimizer, with Applications to Orthogonal Attention, and Optimal Transport
- 31 A Stochastic Variant of Replicator Dynamics in Zero-Sum Games and Its Invariant Measures
- 32 Creation & Annihilation of Spurious Minima in Shallow Neural Networks
- 33 Multiscale Perturbed Gradient Descent: Chaotic Regularization and Heavy-Tailed Limits
- 34 Learning Dynamical Systems
- 35 Some Time, Some Space, and Some Equations: Machine Learning of Model Error in Dynamical Systems
- 36 From Rough Paths to Streamed Data
- 37 Compositional Features and Neural Network Complexity for Dynamical Systems
- 38 Nonparametric Learning of Interaction Kernels in Interacting Particle Systems
- 39 Machine Learning and Dynamical Systems Meet in Reproducing Kernel Hilbert Spaces
