Zobrazeno 1 - 10
of 689 297
pro vyhledávání: '"Has, Christina"'
Autor:
Blaas, Arno, Goliński, Adam, Miller, Andrew, Zappella, Luca, Jacobsen, Jörn-Henrik, Heinze-Deml, Christina
We consider robustness to distribution shifts in the context of diagnostic models in healthcare, where the prediction target $Y$, e.g., the presence of a disease, is causally upstream of the observations $X$, e.g., a biomarker. Distribution shifts ma
Externí odkaz:
http://arxiv.org/abs/2410.19575
Autor:
Sakovich, Anna, Sormani, Christina
We introduce the notion of causally-null-compactifiable space-times which can be canonically converted into a compact timed-metric-spaces using the cosmological time of Andersson-Howard-Galloway and the null distance of Sormani-Vega. We produce a lar
Externí odkaz:
http://arxiv.org/abs/2410.16800
Autor:
Tallent, Nathan, Strube, Jan, Guo, Luanzheng, Lee, Hyungro, Firoz, Jesun, Ghosh, Sayan, Fang, Bo, Bel, Oceane, Spurgeon, Steven, Akers, Sarah, Doty, Christina, Cromwell, Erol
Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information s
Externí odkaz:
http://arxiv.org/abs/2410.16093
AI fairness seeks to improve the transparency and explainability of AI systems by ensuring that their outcomes genuinely reflect the best interests of users. Data augmentation, which involves generating synthetic data from existing datasets, has gain
Externí odkaz:
http://arxiv.org/abs/2410.15470
Autor:
Bashir, Noman, Gohil, Varun, Belavadi, Anagha, Shahrad, Mohammad, Irwin, David, Olivetti, Elsa, Delimitrou, Christina
The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several life
Externí odkaz:
http://arxiv.org/abs/2410.15087
Autor:
Mittmann, Gesa, Laiouar-Pedari, Sara, Mehrtens, Hendrik A., Haggenmüller, Sarah, Bucher, Tabea-Clara, Chanda, Tirtha, Gaisa, Nadine T., Wagner, Mathias, Klamminger, Gilbert Georg, Rau, Tilman T., Neppl, Christina, Compérat, Eva Maria, Gocht, Andreas, Hämmerle, Monika, Rupp, Niels J., Westhoff, Jula, Krücken, Irene, Seidl, Maximillian, Schürch, Christian M., Bauer, Marcus, Solass, Wiebke, Tam, Yu Chun, Weber, Florian, Grobholz, Rainer, Augustyniak, Jaroslaw, Kalinski, Thomas, Hörner, Christian, Mertz, Kirsten D., Döring, Constanze, Erbersdobler, Andreas, Deubler, Gabriele, Bremmer, Felix, Sommer, Ulrich, Brodhun, Michael, Griffin, Jon, Lenon, Maria Sarah L., Trpkov, Kiril, Cheng, Liang, Chen, Fei, Levi, Angelique, Cai, Guoping, Nguyen, Tri Q., Amin, Ali, Cimadamore, Alessia, Shabaik, Ahmed, Manucha, Varsha, Ahmad, Nazeel, Messias, Nidia, Sanguedolce, Francesca, Taheri, Diana, Baraban, Ezra, Jia, Liwei, Shah, Rajal B., Siadat, Farshid, Swarbrick, Nicole, Park, Kyung, Hassan, Oudai, Sakhaie, Siamak, Downes, Michelle R., Miyamoto, Hiroshi, Williamson, Sean R., Holland-Letz, Tim, Schneider, Carolin V., Kather, Jakob Nikolas, Tolkach, Yuri, Brinker, Titus J.
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Glea
Externí odkaz:
http://arxiv.org/abs/2410.15012
Autor:
Baker, William M., Lim, Seunghwan, D'Eugenio, Francesco, Maiolino, Roberto, Ji, Zhiyuan, Arribas, Santiago, Bunker, Andrew J., Carniani, Stefano, Charlot, Stephane, de Graaff, Anna, Hainline, Kevin, Looser, Tobias J., Lyu, Jianwei, Rinaldi, Pierluigi, Robertson, Brant, Schaller, Matthieu, Schaye, Joop, Scholtz, Jan, Ubler, Hannah, Williams, Christina C., Willmer, Christopher N. A., Willott, Chris, Zhu, Yongda
We use NIRSpec/MSA spectroscopy and NIRCam imaging to study a sample of 18 massive ($\log\; M_{*}/M_{\odot} \gt 10\;$dex), central quiescent galaxies at $2\leq z \leq 5$ in the GOODS fields, to investigate their number density, star-formation histori
Externí odkaz:
http://arxiv.org/abs/2410.14773
Autor:
Bukas, Christina, Subramanian, Harshavardhan, See, Fenja, Steinchen, Carina, Ezhov, Ivan, Boosarpu, Gowtham, Asgharpour, Sara, Burgstaller, Gerald, Lehmann, Mareike, Kofler, Florian, Piraud, Marie
High-throughput image analysis in the biomedical domain has gained significant attention in recent years, driving advancements in drug discovery, disease prediction, and personalized medicine. Organoids, specifically, are an active area of research,
Externí odkaz:
http://arxiv.org/abs/2410.14612
Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs' instruction-following capabilities, ra
Externí odkaz:
http://arxiv.org/abs/2410.14582
Autor:
Heo, Juyeon, Heinze-Deml, Christina, Elachqar, Oussama, Ren, Shirley, Nallasamy, Udhay, Miller, Andy, Chan, Kwan Ho Ryan, Narain, Jaya
Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However, LLMs often fail to follow even simple and clear instructions. To imp
Externí odkaz:
http://arxiv.org/abs/2410.14516