3D cellular packings can be trained to realize prescribed stress patterns by updating cell shape indices with a contrastive learning algorithm in a vertex model.
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Single-electron and single-photon stochastic physical neural networks achieve over 97% MNIST test accuracy when trained with empirical outputs in the backward pass using few trials per layer.
Proposes realizing all-optical neural networks via phase-tunable interference, bad-cavity integration, and transient Rabi dynamics in waveguide QED, with simulations showing high accuracy on MNIST and object recognition.
A homodyne photonic tensor processor using TFLN transmitters and Si/SiN circuits demonstrates 1,000-6,000 TOPS throughput with 6-7 bit accuracy at up to 120 Gbaud/s clock rates.
A semi-automatic procedure for aligning defocused SLMs to enable spatial multiplexing and pixel-level conjugation in diffractive neural networks for faster parallel processing.
citing papers explorer
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Training cell stress patterns in 3D cellular packings
3D cellular packings can be trained to realize prescribed stress patterns by updating cell shape indices with a contrastive learning algorithm in a vertex model.
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Training single-electron and single-photon stochastic physical neural networks
Single-electron and single-photon stochastic physical neural networks achieve over 97% MNIST test accuracy when trained with empirical outputs in the backward pass using few trials per layer.
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Optical Neural Networks from Coherent Transient Dynamics in Waveguide QED
Proposes realizing all-optical neural networks via phase-tunable interference, bad-cavity integration, and transient Rabi dynamics in waveguide QED, with simulations showing high accuracy on MNIST and object recognition.
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Homodyne Photonic Tensor Processor exceeds 1,000-TOPS
A homodyne photonic tensor processor using TFLN transmitters and Si/SiN circuits demonstrates 1,000-6,000 TOPS throughput with 6-7 bit accuracy at up to 120 Gbaud/s clock rates.
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Tutorial: A practical guide to the alignment of defocused spatial light modulators for fast diffractive neural networks
A semi-automatic procedure for aligning defocused SLMs to enable spatial multiplexing and pixel-level conjugation in diffractive neural networks for faster parallel processing.