Zobrazeno 1 - 10
of 7 471
pro vyhledávání: '"Ganz, P."'
Autor:
Ginzburg-Ganz, Elinor, Eyal, Ittay, Machlev, Ram, Baimel, Dmitry, Santosh, Leena, Belikov, Juri, Levron, Yoash
We propose an extended demand response program, based on ancillary service for supplying flexible electricity demand. In our proposed scheme, we suggest a broader management model to control the scheduling and power consumption of Bitcoin mining mach
Externí odkaz:
http://arxiv.org/abs/2411.11119
Autor:
Fhima, Jonathan, Avraham, Elad Ben, Nuriel, Oren, Kittenplon, Yair, Ganz, Roy, Aberdam, Aviad, Litman, Ron
Vision-Language (VL) models have garnered considerable research interest; however, they still face challenges in effectively handling text within images. To address this limitation, researchers have developed two approaches. The first method involves
Externí odkaz:
http://arxiv.org/abs/2411.04642
Autor:
Rosbach, Emely, Ammeling, Jonas, Krügel, Sebastian, Kießig, Angelika, Fritz, Alexis, Ganz, Jonathan, Puget, Chloé, Donovan, Taryn, Klang, Andrea, Köller, Maximilian C., Bolfa, Pompei, Tecilla, Marco, Denk, Daniela, Kiupel, Matti, Paraschou, Georgios, Kok, Mun Keong, Haake, Alexander F. H., de Krijger, Ronald R., Sonnen, Andreas F. -P., Kasantikul, Tanit, Dorrestein, Gerry M., Smedley, Rebecca C., Stathonikos, Nikolas, Uhl, Matthias, Bertram, Christof A., Riener, Andreas, Aubreville, Marc
Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, such as confirmation bia
Externí odkaz:
http://arxiv.org/abs/2411.01007
Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration might introduce automation bias, where users uncritically fo
Externí odkaz:
http://arxiv.org/abs/2411.00998
Autor:
Beliveau, Vincent, Kaas, Helene, Prener, Martin, Ladefoged, Claes N., Elliott, Desmond, Knudsen, Gitte M., Pinborg, Lars H., Ganz, Melanie
Natural language processing (NLP) in the medical domain can underperform in real-world applications involving small datasets in a non-English language with few labeled samples and imbalanced classes. There is yet no consensus on how to approach this
Externí odkaz:
http://arxiv.org/abs/2409.20147
Autor:
Ganz, Roy, Elad, Michael
Joint Energy Models (JEMs), while drawing significant research attention, have not been successfully scaled to real-world, high-resolution datasets. We present EB-CLIP, a novel approach extending JEMs to the multimodal vision-language domain using CL
Externí odkaz:
http://arxiv.org/abs/2408.17046
We discuss the non-linear interactions within the VCDM model, a type II minimally modified gravity model with the same number of degrees of freedom as in General Relativity but not connected to the latter by field redefinitions. During an inflationar
Externí odkaz:
http://arxiv.org/abs/2407.02882
Autor:
Domènech, Guillem, Ganz, Alexander
We study secondary gravitational wave production in Horndenski gravity, when the scalar field dominates the very early universe. We find that higher derivative interactions easily dominate the source term on subhorizon scales and significantly enhanc
Externí odkaz:
http://arxiv.org/abs/2406.19950
Autor:
Ganz, Jonathan, Marzahl, Christian, Ammeling, Jonas, Richter, Barbara, Puget, Chloé, Denk, Daniela, Demeter, Elena A., Tabaran, Flaviu A., Wasinger, Gabriel, Lipnik, Karoline, Tecilla, Marco, Valentine, Matthew J., Dark, Michael J., Abele, Niklas, Bolfa, Pompei, Erber, Ramona, Klopfleisch, Robert, Merz, Sophie, Donovan, Taryn A., Jabari, Samir, Bertram, Christof A., Breininger, Katharina, Aubreville, Marc
The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. Dee
Externí odkaz:
http://arxiv.org/abs/2406.19899
Adversarial training aims to defend against *adversaries*: malicious opponents whose sole aim is to harm predictive performance in any way possible - a rather harsh perspective, which we assert results in unnecessarily conservative models. Instead, w
Externí odkaz:
http://arxiv.org/abs/2406.11458