arXiv subjects

Vicente Rocha

Publications and source records attributed to Vicente Rocha.

3 recordsLinked to original sources

arXiv2026

Photonic Extreme Learning Machines using Event-Based detection

Photonic extreme learning machines use random optical propagation, detection nonlinearity, and a trained linear readout for energy-efficient and scalable optical computing. However, conventional intensity readout with CCD or CMOS cameras constrain the dimensionality of the hidden representation space. Here, we experimentally replace intensity detection with event-based camera, whose thresholded log-intensity response provides alternative pixel-wise hidden representations: first-event time, binary activation, and event count. In nonlinear two spiral classification task we obtain accuracies of $93\pm 3\%$ with strong intrinsic generalization, and comparable ridge and pseudo-inverse performance indicating sample-limited effective dimensionality. Regression results reveal sensitivity to systematic optical-intensity drift, identifying stability requirements for future event-based PELMs. These results establish event-based detection as a route toward richer photonic hidden representations while clarifying current limitations.

physics.optics↗

arXiv2025

Probing a theoretical framework for a Photonic Extreme Learning Machine

The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

physics.optics↗

arXiv2024

Optical Extreme Learning Machines with Atomic Vapors

Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.

physics.optics↗