← Back to all news
arXiv Machine Learning By F. Br\"uckerhoff-Pl\"uckelmann, H. Borras, S. U. Hulyal, L. Meyer, X. Ji, J. Hu, J. Sun, B. Klein, F. Ebert, J. Dijkstra, L. McRae, P. Schmidt, T. J. Kippenberg, H. Fr\"oning, W. Pernice

Temporal speckle enables probabilistic weights for uncertainty-aware photonic AI

Read the original on arXiv Machine Learning →

The paper demonstrates that the temporal speckle of broadband light can encode probabilistic weights for Bayesian neural networks, allowing a photonic processor to perform probabilistic convolutions every 37.5 ps. By programming mean and variance into independently amplitude‑bandwidth‑controlled spectral channels, the system delivers uncorrelated samples without heavy random‑number generation. The resulting ultrafast photonic AI classifies blood‑cell images while detecting out‑of‑distribution samples and distinguishing epistemic novelty from aleatoric ambiguity on standard uncertainty benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 4

Constrained Co-Design for Photonic Bayesian Neural Networks

arXiv:2608. 02229v1 Announce Type: new Abstract: 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-critical real-world scenarios.

By Hendrik Borras, Xiao Wang, Bernhard Klein, Robin Janssen, Frank Br\"uckerhoff-Pl\"uckelmann, Wolfram Pernice, Holger Fr\"oning
efficiencybenchmarkssafety
More like this →
arXiv Machine Learning
Jul 23

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.

By Farah Elnakhal, Alberto Marchisio, Nouhaila Innan, Gabriel Falcao, Muhammad Shafique
computer-visionbenchmarks
More like this →
arXiv Computer Vision
Oct 5

One Photon, Many Worlds: Posteriors and Predictions with Single-Photon Cameras

Single-photon avalanche diode (SPAD) cameras produce binary images where each pixel records zero or one photon arrival, making scene reconstruction a fundamentally one-to-many inverse problem. As more binary frames are accumulated, the uncertainty diminishes, transitioning from stochastic to near-deterministic scene understanding. The study introduces a conditional generative framework using a Hypergeometric frame‑thinning process to model this ambiguity and evaluates its impact on tasks such as character recognition, QR code decoding, and facial analysis.

By Haejoon Lee (Carnegie Mellon University), Mohit Gupta (University of Wisconsin-Madison), Vijayakumar Bhagavatula (Carnegie Mellon University), Aswin C. Sankaranarayanan (Carnegie Mellon University)
More like this →