Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Beck, Maximilian"'
Autor:
Alkin, Benedikt, Beck, Maximilian, Pöppel, Korbinian, Hochreiter, Sepp, Brandstetter, Johannes
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM -
Externí odkaz:
http://arxiv.org/abs/2406.04303
Autor:
Beck, Maximilian, Pöppel, Korbinian, Spanring, Markus, Auer, Andreas, Prudnikova, Oleksandra, Kopp, Michael, Klambauer, Günter, Brandstetter, Johannes, Hochreiter, Sepp
In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular the
Externí odkaz:
http://arxiv.org/abs/2405.04517
Autor:
Dinu, Marius-Constantin, Holzleitner, Markus, Beck, Maximilian, Nguyen, Hoan Duc, Huber, Andrea, Eghbal-zadeh, Hamid, Moser, Bernhard A., Pereverzyev, Sergei, Hochreiter, Sepp, Zellinger, Werner
Publikováno v:
International Conference On Learning Representations (ICLR), https://openreview.net/forum?id=M95oDwJXayG, 2023
We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to comp
Externí odkaz:
http://arxiv.org/abs/2305.01281
Publikováno v:
Mechanical Systems and Signal Processing 182 (2023) 109551
A system consisting of a doubly clamped beam with an attached body (slider) free to move along the beam has been studied recently by multiple research groups. Under harmonic base excitation, the system has the capacity to passively adapt itself (by s
Externí odkaz:
http://arxiv.org/abs/2208.00660
Autor:
Gauch, Martin, Beck, Maximilian, Adler, Thomas, Kotsur, Dmytro, Fiel, Stefan, Eghbal-zadeh, Hamid, Brandstetter, Johannes, Kofler, Johannes, Holzleitner, Markus, Zellinger, Werner, Klotz, Daniel, Hochreiter, Sepp, Lehner, Sebastian
Publikováno v:
Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR 199:1043-1064 (2022)
We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace. In experimental and theoretical analyses, we show that models con
Externí odkaz:
http://arxiv.org/abs/2206.03483
Autor:
Alwafai, Zaher1,2 (AUTHOR), Beck, Maximilian Heinz2,3,4 (AUTHOR) maximilian-heinz.beck@charite.de, Fazeli, Sepideh2,5 (AUTHOR), Gürtler, Kathleen2,6 (AUTHOR), Kunz, Christine2,7 (AUTHOR), Singhartinger, Juliane2,8 (AUTHOR), Trojnarska, Dominika2,9 (AUTHOR), Zocholl, Dario10 (AUTHOR), Krankenberg, David Johannes4 (AUTHOR), Blohmer, Jens-Uwe4 (AUTHOR), Sehouli, Jalid2,3 (AUTHOR), Pietzner, Klaus2,3 (AUTHOR)
Publikováno v:
BMC Cancer. 4/3/2024, Vol. 24 Issue 1, p1-9. 9p.
Akademický článek
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Publikováno v:
In Mechanical Systems and Signal Processing 1 January 2023 182
Autor:
Eisenberger, Laura, Mayr, Barbara, Beck, Maximilian, Venek, Verena, Kranzinger, Christina, Menzl, Andrea, Jahn, Inga, Sareban, Mahdi, Oberhoffer-Fritz, Renate, Niebauer, Josef, Böhm, Birgit
Publikováno v:
In Preventive Medicine Reports December 2022 30