Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Kamath, Roshni"'
Autor:
Busch, Florian Peter, Kamath, Roshni, Mitchell, Rupert, Stammer, Wolfgang, Kersting, Kristian, Mundt, Martin
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data. In this work, we show that, in a continual learning setting where confounders may vary in time across tasks, the challenge of mitig
Externí odkaz:
http://arxiv.org/abs/2402.06434
The quest to improve scalar performance numbers on predetermined benchmarks seems to be deeply engraved in deep learning. However, the real world is seldom carefully curated and applications are seldom limited to excelling on test sets. A practical s
Externí odkaz:
http://arxiv.org/abs/2402.04814
Machine learning is typically framed from a perspective of i.i.d., and more importantly, isolated data. In parts, federated learning lifts this assumption, as it sets out to solve the real-world challenge of collaboratively learning a shared model fr
Externí odkaz:
http://arxiv.org/abs/2306.03542
Autor:
Kesselheim, Stefan, Herten, Andreas, Krajsek, Kai, Ebert, Jan, Jitsev, Jenia, Cherti, Mehdi, Langguth, Michael, Gong, Bing, Stadtler, Scarlet, Mozaffari, Amirpasha, Cavallaro, Gabriele, Sedona, Rocco, Schug, Alexander, Strube, Alexandre, Kamath, Roshni, Schultz, Martin G., Riedel, Morris, Lippert, Thomas
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs)
Externí odkaz:
http://arxiv.org/abs/2108.11976
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3df0c724cbc4a9884d2f1055e900565e
https://doi.org/10.1109/icip46576.2022.9897254
https://doi.org/10.1109/icip46576.2022.9897254
