9 citations · 9 across the 6 of their papers we have counts for
7 papers
Two-State Dynamical Synapses as a Physical Substrate for Equilibrium Propagation
Dongshu Liu, Julie Grollier
Equilibrium Propagation (EqProp) updates each synapse by comparing a local term at the free and nudged equilibria. Because these two values occur at different times, a physical imp…
Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal
M. Menshawy, D. Sanz-Hernández, L. Mazza +7
Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote progr…
Experimental quantum reservoir computing with a circuit quantum electrodynamics system
Baptiste Carles, Julien Dudas, Léo Balembois +2
Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimizati…
Convolutions with Radio-Frequency Spin-Diodes
Erwann Plouet, Hanuman Singh, Pankaj Sethi +3
The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF signals vi…
Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement
Julien Dudas, Baptiste Carles, Elie Gouzien +2
Quantum neural networks promise to extend the power of machine learning into the quantum domain, with potential applications ranging from automatic recognition of quantum states to…
Self-Contrastive Forward-Forward Algorithm
Xing Chen, Dongshu Liu, Jeremie Laydevant +1
Agents that operate autonomously benefit from lifelong learning capabilities. However, compatible training algorithms must comply with the decentralized nature of these systems, wh…
Training a multilayer dynamical spintronic network with standard machine learning tools to perform time series classification
Erwan Plouet, Dédalo Sanz-Hernández, Aymeric Vecchiola +2
The ability to process time-series at low energy cost is critical for many applications. Recurrent neural network, which can perform such tasks, are computationally expensive when…