Energy-based learning algorithms for analog computing: a comparative study
Energy-based learning algorithms have recently gained a surge of interest due to their
compatibility with analog (post-digital) hardware. Existing algorithms include contrastive …
compatibility with analog (post-digital) hardware. Existing algorithms include contrastive …
Experimental demonstration of coupled learning in elastic networks
Coupled learning is a contrastive local learning scheme for tuning the properties of
individual elements within a network to achieve desired functionality of the system. It takes …
individual elements within a network to achieve desired functionality of the system. It takes …
Contrastive learning through non-equilibrium memory
Learning algorithms based on backpropagation have enabled transformative technological
advances but alternatives based on local energy-based rules offer benefits in terms of …
advances but alternatives based on local energy-based rules offer benefits in terms of …
[PDF][PDF] Frequency propagation: Multimechanism learning in nonlinear physical networks
We introduce frequency propagation, a learning algorithm for nonlinear physical networks.
In a resistive electrical circuit with variable resistors, an activation current is applied at a set …
In a resistive electrical circuit with variable resistors, an activation current is applied at a set …
Physical effects of learning
Interacting many-body physical systems ranging from neural networks in the brain to folding
proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning …
proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning …
Physical learning of power-efficient solutions
As the size and ubiquity of artificial intelligence and computational machine learning (ML)
models grow, their energy consumption for training and use is rapidly becoming …
models grow, their energy consumption for training and use is rapidly becoming …
Agnostic physics-driven deep learning
This work establishes that a physical system can perform statistical learning without gradient
computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines …
computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines …
Physical neural networks with self-learning capabilities
W Yu, H Guo, J Xiao, J Shen - Science China Physics, Mechanics & …, 2024 - Springer
Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …
using physical systems or materials. These networks harness the distinctive characteristics …
[HTML][HTML] Training self-learning circuits for power-efficient solutions
As the size and ubiquity of artificial intelligence and computational machine learning models
grow, the energy required to train and use them is rapidly becoming economically and …
grow, the energy required to train and use them is rapidly becoming economically and …
Learning Stiffness Tensors in Self‐Activated Solids via a Local Rule
Mechanical metamaterials are often designed with particular properties for specific load‐
bearing functions. Alternatively, this study aims to create a class of active lattice …
bearing functions. Alternatively, this study aims to create a class of active lattice …