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Showing 1–4 of 4 results
Advanced filters: Author: Rumen Dangovski Clear advanced filters

  • Probabilistic machine learning is an emerging computing paradigm which utilizes controllable random sources to encode uncertainty and enable statistical modelling. Here, authors harness quantum vacuum noise as a controllable random source to perform probabilistic inference and image generation.

    • Seou Choi
    • Yannick Salamin
    • Marin Soljačić
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-8

  • Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

    • Peter Y. Lu
    • Rumen Dangovski
    • Marin Soljačić
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-11

  • Deep learning techniques usually require a large quantity of training data and may be challenging for scarce datasets. The authors propose a framework that involves contrastive and transfer learning and reduces data requirements for training while keeping the prediction accuracy.

    • Charlotte Loh
    • Thomas Christensen
    • Marin Soljačić
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-12