Fourth Symposium on Machine Learning and Dynamical Systems
51 videos • 1,455 views • by Fields Institute
1
Machine Learning for Prediction of Earth Climate and Weather
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2
Understanding forecasting with reservoir computing via synchronization
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3
Kernelization of Reservoir Systems
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4
Controlling Chaos Using Edge Computing Hardware
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5
Predicting tipping point with reservoir computing
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6
Training Autonomous Dynamics of a Soft Body: Embedding Bifurcation Structures...
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7
State estimation of complex systems
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8
Learning, approximation and control
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9
Linear Operator Theoretic Framework for Data-Driven Optimal Control:
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10
Several topics at the intersection of control, dynamics, and learning from data
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11
The Operator is the Model
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12
Koopman-based generalization bound for neural networks
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13
On the Barriers of Robust Koopman Learning
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14
Statistical Learning of Transfer Operators and the Infinitesimal Generator
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15
Representation Learning for Dynamical Systems
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16
Combinatorial Topological Dynamics
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17
Identifying nonlinear dynamics with high confidence from sparse data
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18
On finite-dimensional approximations of push-forwards on locally analytic functionals
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19
Closed-Loop Koopman Operator Approximation
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20
Low-dimensional approximations of the conditional law of Volterra processes...
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21
Data-driven reduced order models of forced systems using invariant foliations
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22
Should we Derive or Let the Data Drive? Symbiotizing Data-driven Learning...
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23
Some older, and some current, thoughts on Data and the Modeling of Complex Systems
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24
Operator Learning Without the Adjoint
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25
Learning Port Hamiltonian structures using PINNs type architecture
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26
Provable Posterior Sampling with Score-Based Diffusion through Tilted Transport
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27
Learning and Dynamical Systems: Perspectives from Optimization, Control, and Robotics
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28
Learning Coarse-Grained Dynamics on Graph
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29
Differentiable Programming for Data-driven Modeling, Optimization, and Control
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30
Detecting non-trivial cycles of point clouds and time series data on manifolds
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31
Exploring Cancer Progression: From Static Imaging Data to System Dynamics
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32
Non-smooth dynamics and machine learning
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33
Avoidance of traps for nonconvex stochastic optimization and equilibrium learning in games
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34
Non-Euclidean Generative Modeling
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35
A dynamical systems perspective on measure transport and generative modeling
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36
Phase Transition Theory fo the Score Degradation of Machine Learning Models
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37
Dynamical systems in deep generative modelling
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38
Discovering dynamics and parameters of nonlinear systems from partial observations
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39
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
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40
Continuum Attention for Neural Operators
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41
Ergodic Basis Pursuit induces robust sparse network reconstruction
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42
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
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43
Learning Transfer Operators by Kernel Density Estimation
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44
Graphs of Random Matrices in Deep Learning
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45
Data-adaptive RKHS regularization for learning kernels in operators
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46
Ensemble forecasts in reproducing kernel Hilbert space family
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47
Equivariant learning through invariant theory
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48
Ensemble forecasts in reproducing kernel Hilbert space family
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49
Orbit hierarchies and long-term predictability in chaotic systems
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50
Simplicity bias, algorithmic probability, and time series
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51
On Bridging Machine Learning, Dynamical Systems, and Algorithmic Information Theory
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