Zobrazeno 1 - 10
of 554 431
pro vyhledávání: '"Nathan, A"'
Autor:
Berijanian, Maryam, Dork, Spencer, Singh, Kuldeep, Millikan, Michael Riley, Riggs, Ashlin, Swaminathan, Aadarsh, Gibbs, Sarah L., Friedman, Scott E., Brugnone, Nathan
Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from
Externí odkaz:
http://arxiv.org/abs/2409.18911
Similarity measures are fundamental tools for quantifying the alignment between artificial and biological systems. However, the diversity of similarity measures and their varied naming and implementation conventions makes it challenging to compare ac
Externí odkaz:
http://arxiv.org/abs/2409.18333
Autor:
Hao, Yuexing, Holmes, Jason M., Hobson, Jared, Bennett, Alexandra, Ebner, Daniel K., Routman, David M., Shiraishi, Satomi, Patel, Samir H., Yu, Nathan Y., Hallemeier, Chris L., Ball, Brooke E., Waddle, Mark R., Liu, Wei
In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden
Externí odkaz:
http://arxiv.org/abs/2409.18290
Conformal Prediction methods have finite-sample distribution-free marginal coverage guarantees. However, they generally do not offer conditional coverage guarantees, which can be important for high-stakes decisions. In this paper, we propose a novel
Externí odkaz:
http://arxiv.org/abs/2409.17466
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning fr
Externí odkaz:
http://arxiv.org/abs/2409.17367
Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. Howe
Externí odkaz:
http://arxiv.org/abs/2409.17201
Autor:
Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung, Salehi, Mohammadreza, Muennighoff, Niklas, Lo, Kyle, Soldaini, Luca, Lu, Jiasen, Anderson, Taira, Bransom, Erin, Ehsani, Kiana, Ngo, Huong, Chen, YenSung, Patel, Ajay, Yatskar, Mark, Callison-Burch, Chris, Head, Andrew, Hendrix, Rose, Bastani, Favyen, VanderBilt, Eli, Lambert, Nathan, Chou, Yvonne, Chheda, Arnavi, Sparks, Jenna, Skjonsberg, Sam, Schmitz, Michael, Sarnat, Aaron, Bischoff, Byron, Walsh, Pete, Newell, Chris, Wolters, Piper, Gupta, Tanmay, Zeng, Kuo-Hao, Borchardt, Jon, Groeneveld, Dirk, Dumas, Jen, Nam, Crystal, Lebrecht, Sophie, Wittlif, Caitlin, Schoenick, Carissa, Michel, Oscar, Krishna, Ranjay, Weihs, Luca, Smith, Noah A., Hajishirzi, Hannaneh, Girshick, Ross, Farhadi, Ali, Kembhavi, Aniruddha
Today's most advanced multimodal models remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed models into open ones. As a result, the
Externí odkaz:
http://arxiv.org/abs/2409.17146
Autor:
Giannakopoulos, Christos, Vergès, Clara, Ade, P. A. R., Ahmed, Zeeshan, Amiri, Mandana, Barkats, Denis, Thakur, Ritoban Basu, Bischoff, Colin A., Beck, Dominic, Bock, James J., Boenish, Hans, Buza, Victor, Cheshire IV, James R., Connors, Jake, Cornelison, James, Crumrine, Michael, Cukierman, Ari Jozef, Denison, Edward, Dierickx, Marion, Duband, Lionel, Eiben, Miranda, Elwood, Brodi D., Fatigoni, Sofia, Filippini, Jeff P., Fortes, Antonio, Gao, Min, Goeckner-Wald, Neil, Goldfinger, David C., Grayson, James A., Grimes, Paul K., Hall, Grantland, Halal, George, Halpern, Mark, Hand, Emma, Harrison, Sam A., Henderson, Shawn, Hubmayr, Johannes, Hui, Howard, Irwin, Kent D., Kang, Jae Hwan, Karkare, Kirit S., Kefeli, Sinan, Kovac, J. M., Kuo, Chao-Lin, Lau, King, Lautzenhiser, Margaret, Lennox, Amber, Liu, Tongtian, Megerian, Koko G., Miller, Oliver, Minutolo, Lorenzo, Moncelsi, Lorenzo, Nakato, Yuka, Nguyen, H. T., O'brient, Roger, Patel, Anika, Petroff, Matthew A., Polish, Anna R., Precup, Nathan, Prouve, Thomas, Pryke, Clement, Reintsema, Carl D., Romand, Thibault, Salatino, Maria, Schillaci, Alessandro, Schmitt, Benjamin, Singari, Baibhav, Soliman, Ahmed, Germaine, Tyler St, Steiger, Aaron, Steinbach, Bryan, Sudiwala, Rashmi, Thompson, Keith L., Tsai, Calvin, Tucker, Carole, Turner, Anthony D., Vieregg, Abigail G., Wandui, Albert, Weber, Alexis C., Willmert, Justin, Wu, Wai Ling K., Yang, Hung-I, Yu, Cyndia, Zeng, Lingzhen, Zhang, Cheng, Zhang, Silvia
The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Con
Externí odkaz:
http://arxiv.org/abs/2409.16440
Autor:
Niu, Zhijing, Schäfer, Vera M., Zhang, Haoqing, Wagner, Cameron, Taylor, Nathan R., Young, Dylan J., Song, Eric Yilun, Chu, Anjun, Rey, Ana Maria, Thompson, James K.
Quantum simulation and metrology with atoms, ions, and molecules often rely on using light fields to manipulate their internal states. The absorbed momentum from the light fields can induce spin-orbit coupling and associated motional-induced (Doppler
Externí odkaz:
http://arxiv.org/abs/2409.16265
Autor:
Kerner, Hannah, Chaudhari, Snehal, Ghosh, Aninda, Robinson, Caleb, Ahmad, Adeel, Choi, Eddie, Jacobs, Nathan, Holmes, Chris, Mohr, Matthias, Dodhia, Rahul, Ferres, Juan M. Lavista, Marcus, Jennifer
Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually. Machine learning (ML) methods for automatically extracting field boundaries from remotely sensed images could help real
Externí odkaz:
http://arxiv.org/abs/2409.16252