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Found 6 papers, 1 papers with code
Fast Autofocusing using Tiny Transformer Networks for Digital Holographic Microscopy
Tiny DL models are proposed and compared such as a tiny Vision Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT).
Boolean learning under noise-perturbations in hardware neural networks
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate.
Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
Photonic waveguides are prime candidates for integrated and parallel photonic interconnects.
Reservoir-size dependent learning in analogue neural networks
However, important questions regarding impact of reservoir size and learning routines on the convergence-speed during learning remain unaddressed.
Fundamental aspects of noise in analog-hardware neural networks
The system under study consists of noisy linear nodes, and we investigate the signal-to-noise ratio at the network's outputs which is the upper limit to such a system's computing accuracy.
Reinforcement Learning in a large scale photonic Recurrent Neural Network
Realizing photonic Neural Networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware was lacking so far.
