Add to ORKGA physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields
Is it possible to generally construct a dynamical system to simulate a black system without recoveri...
Hamiltonians can generate Artificial Neural Dynamical systems dependent on time. Classical methods f...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained ...
Neural networks are currently implemented on digital Von Neumann machines, which do not fully levera...
AbstractUseful computation can be performed by systematically exploiting the phenomenology of nonlin...
We associate learning in living systems with the shaping of the velocity vector field of a dynamical...
The process of machine learning can be considered in two stages model selection and parameter estima...
Machine learning algorithms, and more in par-ticular neural networks, arguably experience a revoluti...
At a first glance, artificial neural networks, with engineered learning algorithms and carefully cho...
Compared to biological systems, existing learning systems lack the ability to learn autonomously, es...
Nonlinear photonic delay systems present interesting implementation platforms for machine learning m...
The process of machine learning can be considered in two stages: model selection and parameter estim...
Learning in physical neural systems must rely on learning rules that are local in both space and tim...
To describe learning, as an alternative to a neural network recently dynamical systems were introduc...
Is it possible to generally construct a dynamical system to simulate a black system without recoveri...
Hamiltonians can generate Artificial Neural Dynamical systems dependent on time. Classical methods f...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained ...
Neural networks are currently implemented on digital Von Neumann machines, which do not fully levera...
AbstractUseful computation can be performed by systematically exploiting the phenomenology of nonlin...
We associate learning in living systems with the shaping of the velocity vector field of a dynamical...
The process of machine learning can be considered in two stages model selection and parameter estima...
Machine learning algorithms, and more in par-ticular neural networks, arguably experience a revoluti...
At a first glance, artificial neural networks, with engineered learning algorithms and carefully cho...
Compared to biological systems, existing learning systems lack the ability to learn autonomously, es...
Nonlinear photonic delay systems present interesting implementation platforms for machine learning m...
The process of machine learning can be considered in two stages: model selection and parameter estim...
Learning in physical neural systems must rely on learning rules that are local in both space and tim...
To describe learning, as an alternative to a neural network recently dynamical systems were introduc...
Is it possible to generally construct a dynamical system to simulate a black system without recoveri...
Hamiltonians can generate Artificial Neural Dynamical systems dependent on time. Classical methods f...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...