Zobrazeno 1 - 10
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pro vyhledávání: '"Hunter, Lawrence E."'
The evolution of biological neural systems has led to both modularity and sparse coding, which enables efficiency in energy usage, and robustness across the diversity of tasks in the lifespan. In contrast, standard neural networks rely on dense, non-
Externí odkaz:
http://arxiv.org/abs/2410.08003
Natural language processing has seen rapid progress over the past decade. Due to the speed of developments, some practices get established without proper evaluation. Considering one such case and focusing on reading comprehension, we ask our first re
Externí odkaz:
http://arxiv.org/abs/2406.16779
The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 different LMs on 10 probing English datasets -- 4 template-based and 6 templ
Externí odkaz:
http://arxiv.org/abs/2402.00123
Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model's parametric knowledge, little robustness to noise, and a l
Externí odkaz:
http://arxiv.org/abs/2401.18001
Both standalone language models (LMs) as well as LMs within downstream-task systems have been shown to generate statements which are factually untrue. This problem is especially severe for low-resource languages, where training data is scarce and of
Externí odkaz:
http://arxiv.org/abs/2310.10583
Autor:
Callahan, Tiffany J., Tripodi, Ignacio J., Stefanski, Adrianne L., Cappelletti, Luca, Taneja, Sanya B., Wyrwa, Jordan M., Casiraghi, Elena, Matentzoglu, Nicolas A., Reese, Justin, Silverstein, Jonathan C., Hoyt, Charles Tapley, Boyce, Richard D., Malec, Scott A., Unni, Deepak R., Joachimiak, Marcin P., Robinson, Peter N., Mungall, Christopher J., Cavalleri, Emanuele, Fontana, Tommaso, Valentini, Giorgio, Mesiti, Marco, Gillenwater, Lucas A., Santangelo, Brook, Vasilevsky, Nicole A., Hoehndorf, Robert, Bennett, Tellen D., Ryan, Patrick B., Hripcsak, George, Kahn, Michael G., Bada, Michael, Baumgartner Jr, William A., Hunter, Lawrence E.
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowle
Externí odkaz:
http://arxiv.org/abs/2307.05727
Autor:
Callahan, Tiffany J., Stefanski, Adrianne L., Wyrwa, Jordan M., Zeng, Chenjie, Ostropolets, Anna, Banda, Juan M., Baumgartner Jr., William A., Boyce, Richard D., Casiraghi, Elena, Coleman, Ben D., Collins, Janine H., Deakyne-Davies, Sara J., Feinstein, James A., Haendel, Melissa A., Lin, Asiyah Y., Martin, Blake, Matentzoglu, Nicolas A., Meeker, Daniella, Reese, Justin, Sinclair, Jessica, Taneja, Sanya B., Trinkley, Katy E., Vasilevsky, Nicole A., Williams, Andrew, Zhang, Xingman A., Denny, Joshua C., Robinson, Peter N., Ryan, Patrick, Hripcsak, George, Bennett, Tellen D., Hunter, Lawrence E., Kahn, Michael G.
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry o
Externí odkaz:
http://arxiv.org/abs/2209.04732
Autor:
Callahan, Tiffany J., Stefanski, Adrianne L., Kim, Jin-Dong, Baumgartner Jr., William A., Wyrwa, Jordan M., Hunter, Lawrence E.
Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of hum
Externí odkaz:
http://arxiv.org/abs/2207.14294
Autor:
Joslyn, Cliff A., Aksoy, Sinan, Callahan, Tiffany J., Hunter, Lawrence E., Jefferson, Brett, Praggastis, Brenda, Purvine, Emilie A. H., Tripodi, Ignacio J.
As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other that of higher-order mathematical abstractions from algebra, lattice th
Externí odkaz:
http://arxiv.org/abs/2003.11782