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cs.LG2026
Constrained Co-Design for Photonic Bayesian Neural Networks
Hendrik Borras, Xiao Wang, Bernhard Klein +4
Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety…
physics.app-ph2026
Probabilistic Photonic Computing
Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri +8
Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is cha…
physics.app-ph2026
Multidimensional photonic computing
Ivonne Bente, Shabnam Taheriniya, Francesco Lenzini +5
The rapidly increasing demands for computational throughput, bandwidth, and memory capacity fueled by breakthroughs in machine learning pose substantial challenges for conventional…