Zobrazeno 1 - 10
of 9 725
pro vyhledávání: '"A, Kappel"'
Autor:
Sushma, Neeraj Mohan, Tian, Yudou, Mestha, Harshvardhan, Colombo, Nicolo, Kappel, David, Subramoney, Anand
Deep state-space models (Deep SSMs) have shown capabilities for in-context learning on autoregressive tasks, similar to transformers. However, the architectural requirements and mechanisms enabling this in recurrent networks remain unclear. This stud
Externí odkaz:
http://arxiv.org/abs/2410.11687
Autor:
Hahlbohm, Florian, Friederichs, Fabian, Weyrich, Tim, Franke, Linus, Kappel, Moritz, Castillo, Susana, Stamminger, Marc, Eisemann, Martin, Magnor, Marcus
3D Gaussian Splats (3DGS) have proven a versatile rendering primitive, both for inverse rendering as well as real-time exploration of scenes. In these applications, coherence across camera frames and multiple views is crucial, be it for robust conver
Externí odkaz:
http://arxiv.org/abs/2410.08129
Autor:
Fokam, Cabrel Teguemne, Nazeer, Khaleelulla Khan, König, Lukas, Kappel, David, Subramoney, Anand
The increasing size of deep learning models has created the need for more efficient alternatives to the standard error backpropagation algorithm, that make better use of asynchronous, parallel and distributed computing. One major shortcoming of backp
Externí odkaz:
http://arxiv.org/abs/2410.05985
Autor:
Huber, Johann, Hélénon, François, Kappel, Mathilde, Páez-Ubieta, Ignacio de Loyola, Puente, Santiago T., Gil, Pablo, Amar, Faïz Ben, Doncieux, Stéphane
Recent advances in AI have led to significant results in robotic learning, but skills like grasping remain partially solved. Many recent works exploit synthetic grasping datasets to learn to grasp unknown objects. However, those datasets were generat
Externí odkaz:
http://arxiv.org/abs/2410.02319
Autor:
Kappel, Ellen S.
Publikováno v:
Oceanography, 2024 Sep 01. 37(3), 5-5.
Externí odkaz:
https://www.jstor.org/stable/27333917
Autor:
Arslan, Serkan, Kappel, Micha, Valero, Adrià Canós, Tran, Thu Huong T., Karst, Julian, Christ, Philipp, Hohenester, Ulrich, Weiss, Thomas, Giessen, Harald, Hentschel, Mario
Traditional nanophotonic sensing schemes utilize evanescent fields in dielectric or metallic nanoparticles, which confine far-field radiation in dispersive and lossy media. Apart from the lack of a well-defined sensing volume that can be accompanied
Externí odkaz:
http://arxiv.org/abs/2407.02331
Autor:
Kappel, Moritz, Hahlbohm, Florian, Scholz, Timon, Castillo, Susana, Theobalt, Christian, Eisemann, Martin, Golyanik, Vladislav, Magnor, Marcus
Dynamic reconstruction and spatiotemporal novel-view synthesis of non-rigidly deforming scenes recently gained increased attention. While existing work achieves impressive quality and performance on multi-view or teleporting camera setups, most metho
Externí odkaz:
http://arxiv.org/abs/2406.10078
Autor:
Mukherji, Rishav, Schöne, Mark, Nazeer, Khaleelulla Khan, Mayr, Christian, Kappel, David, Subramoney, Anand
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient. Event-based architectures such as spiking neural networks (SNNs) naturally exhibit activity sparsity, and many methods exist to sparsif
Externí odkaz:
http://arxiv.org/abs/2405.00433
Autor:
Schöne, Mark, Sushma, Neeraj Mohan, Zhuge, Jingyue, Mayr, Christian, Subramoney, Anand, Kappel, David
Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when
Externí odkaz:
http://arxiv.org/abs/2404.18508
Autor:
Hahlbohm, Florian, Franke, Linus, Kappel, Moritz, Castillo, Susana, Stamminger, Marc, Magnor, Marcus
We introduce a new approach for reconstruction and novel-view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene repr
Externí odkaz:
http://arxiv.org/abs/2403.16862