Zobrazeno 1 - 10
of 3 323
pro vyhledávání: '"Alber, P."'
Autor:
Rosenbaum, Gabriel R., Jiang, Lavender Yao, Sheth, Ivaxi, Stryker, Jaden, Alyakin, Anton, Alber, Daniel Alexander, Goff, Nicolas K., Kwon, Young Joon Fred, Markert, John, Nasir-Moin, Mustafa, Niehues, Jan Moritz, Sangwon, Karl L., Yang, Eunice, Oermann, Eric Karl
Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has le
Externí odkaz:
http://arxiv.org/abs/2412.10982
Publikováno v:
Machine Learning and Knowledge Discovery in Databases.Applied Data Science Track, vol 14950, Springer (2024) 116-132
One of the most promising use-cases for machine learning in industrial manufacturing is the early detection of defective products using a quality control system. Such a system can save costs and reduces human errors due to the monotonous nature of vi
Externí odkaz:
http://arxiv.org/abs/2411.14953
Autor:
Sextro, Marvin, Dernbach, Gabriel, Standvoss, Kai, Schallenberg, Simon, Klauschen, Frederick, Müller, Klaus-Robert, Alber, Maximilian, Ruff, Lukas
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent adv
Externí odkaz:
http://arxiv.org/abs/2411.07643
Autor:
Fourney, Adam, Bansal, Gagan, Mozannar, Hussein, Tan, Cheng, Salinas, Eduardo, Erkang, Zhu, Niedtner, Friederike, Proebsting, Grace, Bassman, Griffin, Gerrits, Jack, Alber, Jacob, Chang, Peter, Loynd, Ricky, West, Robert, Dibia, Victor, Awadallah, Ahmed, Kamar, Ece, Hosn, Rafah, Amershi, Saleema
Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achieve this vision, AI agents must effectively plan, perform multi-step rea
Externí odkaz:
http://arxiv.org/abs/2411.04468
Autor:
Vishwanath, Krithik, Stryker, Jaden, Alyakin, Anton, Alber, Daniel Alexander, Oermann, Eric Karl
Language models (LMs) have demonstrated expert-level reasoning and recall abilities in medicine. However, computational costs and privacy concerns are mounting barriers to wide-scale implementation. We introduce a parsimonious adaptation of phi-3-min
Externí odkaz:
http://arxiv.org/abs/2410.09019
We present and analyze a two-level restricted additive Schwarz (RAS) preconditioner for heterogeneous Helmholtz problems, based on a multiscale spectral generalized finite element method (MS-GFEM) proposed in [C. Ma, C. Alber, and R. Scheichl, SIAM.
Externí odkaz:
http://arxiv.org/abs/2409.06533
Autor:
Dippel, Jonas, Prenißl, Niklas, Hense, Julius, Liznerski, Philipp, Winterhoff, Tobias, Schallenberg, Simon, Kloft, Marius, Buchstab, Oliver, Horst, David, Alber, Maximilian, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick
While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for co
Externí odkaz:
http://arxiv.org/abs/2406.14866
We present a multiscale mixed finite element method for solving second order elliptic equations with general $L^{\infty}$-coefficients arising from flow in highly heterogeneous porous media. Our approach is based on a multiscale spectral generalized
Externí odkaz:
http://arxiv.org/abs/2403.16714
Autor:
Bialy, Nikki, Alber, Frank, Andrews, Brenda, Angelo, Michael, Beliveau, Brian, Bintu, Lacramioara, Boettiger, Alistair, Boehm, Ulrike, Brown, Claire M., Maina, Mahmoud Bukar, Chambers, James J., Cimini, Beth A., Eliceiri, Kevin, Errington, Rachel, Faklaris, Orestis, Gaudreault, Nathalie, Germain, Ronald N., Goscinski, Wojtek, Grunwald, David, Halter, Michael, Hanein, Dorit, Hickey, John W., Lacoste, Judith, Laude, Alex, Lundberg, Emma, Ma, Jian, Malacrida, Leonel, Moore, Josh, Nelson, Glyn, Neumann, Elizabeth Kathleen, Nitschke, Roland, Onami, Shuichi, Pimentel, Jaime A., Plant, Anne L., Radtke, Andrea J., Sabata, Bikash, Schapiro, Denis, Schöneberg, Johannes, Spraggins, Jeffrey M., Sudar, Damir, Vierdag, Wouter-Michiel Adrien Maria, Volkmann, Niels, Wählby, Carolina, Siyuan, Wang, Yaniv, Ziv, Strambio-De-Castillia, Caterina
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health.
Externí odkaz:
http://arxiv.org/abs/2401.13022
Autor:
Dippel, Jonas, Feulner, Barbara, Winterhoff, Tobias, Milbich, Timo, Tietz, Stephan, Schallenberg, Simon, Dernbach, Gabriel, Kunft, Andreas, Heinke, Simon, Eich, Marie-Lisa, Ribbat-Idel, Julika, Krupar, Rosemarie, Anders, Philipp, Prenißl, Niklas, Jurmeister, Philipp, Horst, David, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick, Alber, Maximilian
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to gene
Externí odkaz:
http://arxiv.org/abs/2401.04079