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Found 13 papers, 0 papers with code
Automatic Anonymization of Swiss Federal Supreme Court Rulings
Releasing court decisions to the public relies on proper anonymization to protect all involved parties, where necessary.
Convergence and scaling of Boolean-weight optimization for hardware reservoirs
Hardware implementation of neural network are an essential step to implement next generation efficient and powerful artificial intelligence solutions.
Noise mitigation strategies in physical feedforward neural networks
They therefore are prone to noise with a variety of statistical and architectural properties, and effective strategies leveraging network-inherent assets to mitigate noise in an hardware-efficient manner are important in the pursuit of next generation neural network hardware.
Understanding and mitigating noise in trained deep neural networks
Here, we determine for the first time the propagation of noise in deep neural networks comprising noisy nonlinear neurons in trained fully connected layers.
A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser
Finally, the digital analog conversion can be realized with a standard deviation of only 5. 4 10^-2.
Human action recognition with a large-scale brain-inspired photonic computer
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e. g. brain-computer interface and surveillance.
Bayesian optimisation of large-scale photonic reservoir computers
We test this approach on a previously reported large-scale experimental system, compare it to the commonly used grid search, and report notable improvements in performance and the number of experimental iterations required to optimise the hyper-parameters.
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.
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort.
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.
