Zobrazeno 1 - 10
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pro vyhledávání: '"LORENZ, K. A."'
Over the past year, Speculative Decoding has gained popularity as a technique for accelerating Large Language Model inference. While several methods have been introduced, most struggle to deliver satisfactory performance at batch sizes typical for da
Externí odkaz:
http://arxiv.org/abs/2411.05894
Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time applications. To address this, we propose a framework with end-to-end training of
Externí odkaz:
http://arxiv.org/abs/2310.03507
Autor:
Mieszczynski, C., Wyszkowska, E., Jozwik, P., Skrobas, K., K.Stefanska-Skrobas, Barlak, M., Ratajczak, R., Kosinska, A., Chrominski, W., Lorenz, K.
Publikováno v:
In Applied Surface Science 15 December 2024 676
Autor:
Muller, Lorenz K.
Deep convolutional neural networks can enhance images taken with small mobile camera sensors and excel at tasks like demoisaicing, denoising and super-resolution. However, for practical use on mobile devices these networks often require too many FLOP
Externí odkaz:
http://arxiv.org/abs/2105.08470
Publikováno v:
In Materials Science in Semiconductor Processing 1 November 2023 166
Autor:
Barbosa, M. B., Correia, J. G., Lorenz, K., Fenta, A. S., Schell, J., Teixeira, R., Nogales, E., Méndez, B., Stroppa, A., Araújo, J. P.
Finding suitable p-type dopants, as well as reliable doping and characterization methods for the emerging wide bandgap semiconductor $\beta$-$Ga_2O_3$ could strongly influence and contribute to the development of the next generation of power electron
Externí odkaz:
http://arxiv.org/abs/1908.09569
Autor:
Ajia, I. A., Miranda, S. M. C., Franco, N., Alves, E., Lorenz, K., O'Donnell, K. P., Roqan, I. S.
Publikováno v:
Material Sci & Eng. 2018;2(6):193-197
We report on structural and optical properties of InGaN/GaN thin films, with a 0.46o misalignment between the surface and the (0001) plane, which were grown by metal-organic chemical vapor deposition (MOCVD) on 0.34o miscut sapphire substrates. X-ray
Externí odkaz:
http://arxiv.org/abs/1902.06592
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state-depe
Externí odkaz:
http://arxiv.org/abs/1807.05128
Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses trans
Externí odkaz:
http://arxiv.org/abs/1709.05484
Publikováno v:
Disability & Rehabilitation; Sep2024, Vol. 46 Issue 18, p4216-4225, 10p