407 citations · 1.7k across the 34 of their papers we have counts for
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Noise mitigation strategies in physical feedforward neural networks
Nadezhda Semenova, Daniel Brunner
Physical neural networks are promising candidates for next generation artificial intelligence hardware. In such architectures, neurons and connections are physically realized and d…
Understanding and mitigating noise in trained deep neural networks
Nadezhda Semenova, Laurent Larger, Daniel Brunner
Deep neural networks unlocked a vast range of new applications by solving tasks of which many were previously deemed as reserved to higher human intelligence. One of the developmen…
A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser
Xavier Porte, Anas Skalli, Nasibeh Haghighi +3
Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing in a number of fundamental aspect…
Human action recognition with a large-scale brain-inspired photonic computer
Piotr Antonik, Nicolas Marsal, Daniel Brunner +1
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e.g. brain-computer interface and surveillance. Deep learn…
Bayesian optimisation of large-scale photonic reservoir computers
Piotr Antonik, Nicolas Marsal, Daniel Brunner +1
Introduction. Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experimen…
Boolean learning under noise-perturbations in hardware neural networks
Louis Andreoli, Xavier Porte, Stéphane Chrétien +3
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems ha…