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Showing 1–3 of 3 results
Advanced filters: Author: Jérémie Laydevant Clear advanced filters

  • Local learning algorithms still fall short of matching the performance of traditional backpropagation. Here, the authors developed the Self-Contrastive ForwardForward algorithm, which leverages contrastive learning principles to enhance unsupervised forward-forward training.

    • Xing Chen
    • Dongshu Liu
    • Julie Grollier
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-13

  • Ising machines have been usually applied to predefined combinatorial problems due to their distinct physical properties. The authors introduce an approach that utilizes equilibrium propagation for the training of Ising machines and achieves high accuracy performance on classification tasks.

    • Jérémie Laydevant
    • Danijela Marković
    • Julie Grollier
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-14

  • Neural networks on analog physical devices often struggle with low computational precision. Here, authors developed an approach that enables neural networks to effectively exploit highly stochastic systems, achieving high performance even under an extremely low signal-to-noise ratio.

    • Shi-Yuan Ma
    • Tianyu Wang
    • Peter L. McMahon
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12