16 citations · 21 across the 6 of their papers we have counts for
6 papers
Neuromorphic computing with optomechanical oscillators
Andrea Gaspari, Rémi Avriller, Florian Marquardt +1
The increasing resource demands of artificial neural networks have prompted the exploration of novel platforms better suited for machine learning. In this context, phase oscillator…
Dependence of Equilibrium Propagation Training Success on Network Architecture
Qingshan Wang, Clara C. Wanjura, Florian Marquardt
The rapid rise of artificial intelligence has led to an unsustainable growth in energy consumption. This has motivated progress in neuromorphic computing and physics-based training…
Training nonlinear optical neural networks with Scattering Backpropagation
Nicola Dal Cin, Florian Marquardt, Clara C. Wanjura
As deep learning applications continue to deploy increasingly large artificial neural networks, the associated high energy demands are creating a need for alternative neuromorphic…
Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity
Clara C. Wanjura, Florian Marquardt
The widespread adoption of machine learning and artificial intelligence in all branches of science and technology has created a need for energy-efficient, alternative hardware plat…
Training of Physical Neural Networks
Ali Momeni, Babak Rahmani, Benjamin Scellier +25
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research…
Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation
Qingshan Wang, Clara C. Wanjura, Florian Marquardt
Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics…