mlsanityData to 7 Oct 2026

Dynamical learning of dynamics

arXiv 1902.02875 ↗First posted 7 Feb 2019q-bio.NCcited by 16

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Abstract (arXiv)

The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here we show that fixed-weight neural networks can learn to generate required dynamics by imitation. After appropriate weight pretraining, the networks quickly and dynamically adapt to learn new tasks and thereafter continue to achieve them without further teacher feedback. We explain this ability and illustrate it with a variety of target dynamics, ranging from oscillatory trajectories to driven and chaotic dynamical systems.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 4; 2025: 0; 2026: 2.

16 papers.

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  1. 2021Adaptable Hamiltonian neural networkscited by 21
  2. 2022W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecastingcited by 12
  3. 2023Machine-learning parameter tracking with partial state observationcited by 4
  4. 2021Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networkscited by 3
  5. 2021Learning Hamiltonian dynamics by reservoir computercited by 2
  6. 2022Digital twins of nonlinear dynamical systemscited by 2
  7. 2022Emergence of a stochastic resonance in machine learningcited by 2
  8. 2024Expressivity of Neural Networks with Random Weights and Learned Biasescited by 2
  9. 2024Learning to learn ecosystems from limited data -- a meta-learning approachcited by 2
  10. 2024Reconstructing dynamics from sparse observations with no training on target systemcited by 2
  11. 2023Digital twins of nonlinear dynamical systems: A perspectivecited by 1
  12. 2022Knowledge-based Deep Learning for Modeling Chaotic Systemscited by 0
4 more
  1. 2023Variability of echo state network prediction horizon for partially observed dynamical systemscited by 0
  2. 2024Adaptive control of recurrent neural networks using conceptorscited by 0
  3. 2026Vestibular reservoir computingcited by 0
  4. 2026Inferring bifurcation diagrams of two distinct chaotic systems by a single machinecited by 0