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pro vyhledávání: '"Ness, P"'
Survival analysis is a classic problem in statistics with important applications in healthcare. Most machine learning models for survival analysis are black-box models, limiting their use in healthcare settings where interpretability is paramount. Mo
Externí odkaz:
http://arxiv.org/abs/2411.05923
Autor:
Siam, Shakhrul Iman, Ahn, Hyunho, Liu, Li, Alam, Samiul, Shen, Hui, Cao, Zhichao, Shroff, Ness, Krishnamachari, Bhaskar, Srivastava, Mani, Zhang, Mi
Publikováno v:
ACM Trans. Sen. Netw.(August 2024)
The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT r
Externí odkaz:
http://arxiv.org/abs/2410.19998
Multi-objective Markov Decision Processes (MDPs) are receiving increasing attention, as real-world decision-making problems often involve conflicting objectives that cannot be addressed by a single-objective MDP. The Pareto front identifies the set o
Externí odkaz:
http://arxiv.org/abs/2410.15557
The rheology of dense suspensions lacks a universal description due to the involvement of a wide variety of parameters, ranging from the physical properties of solid particles to the nature of the external deformation or applied stress. While the for
Externí odkaz:
http://arxiv.org/abs/2410.15216
The denoising diffusion model has recently emerged as a powerful generative technique, capable of transforming noise into meaningful data. While theoretical convergence guarantees for diffusion models are well established when the target distribution
Externí odkaz:
http://arxiv.org/abs/2410.13746
Autor:
Das, Pradosh Barun, Zucker, Daniel B., De Silva, Gayandhi M., Borsato, Nicholas W., Mura-Guzmán, Aldo, Buder, Sven, Ness, Melissa, Nordlander, Thomas, Casey, Andrew R., Martell, Sarah L., Bland-Hawthorn, Joss, de Grijs, Richard, Freeman, Ken C., Kos, Janez, Stello, Dennis, Lewis, Geraint F., Hayden, Michael R., Sharma, Sanjib
Analysing stellar parameters and abundances from nearly one million Gaia DR3 Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise rati
Externí odkaz:
http://arxiv.org/abs/2410.12272
Autor:
Lu, Yuxi, Colman, Isabel L., Sayeed, Maryum, Amard, Louis, Buder, Sven, Manea, Catherine, Hattori, Soichiro, Pinsonneault, Marc H., Price-Whelan, Adrian M., Bedell, Megan, Nidever, David, Johnson, Jennifer A., Ness, Melissa, Angus, Ruth, Claytor, Zachary R., Horta, Danny, Behmard, Aida
The existence of high-$\alpha$ stars with inferred ages < 6 Gyr has been confirmed recently with large spectroscopic and photometric surveys. However, stellar mergers or binary interactions can induce properties associated with young ages, such as hi
Externí odkaz:
http://arxiv.org/abs/2410.02962
Autor:
Buder, S., Kos, J., Wang, E. X., McKenzie, M., Howell, M., Martell, S. L., Hayden, M. R., Zucker, D. B., Nordlander, T., Montet, B. T., Traven, G., Bland-Hawthorn, J., De Silva, G. M., Freeman, K. C., Lewis, G. F., Lind, K., Sharma, S., Simpson, J. D., Stello, D., Zwitter, T., Amarsi, A. M., Armstrong, J. J., Banks, K., Beavis, M. A., Beeson, K., Chen, B., Ciucă, I., Da Costa, G. S., de Grijs, R., Martin, B., Nataf, D. M., Ness, M. K., Rains, A. D., Scarr, T., Vogrinčič, R., Wang, Z., Wittenmyer, R. A., Xie, Y., Collaboration, The GALAH
The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data rel
Externí odkaz:
http://arxiv.org/abs/2409.19858
Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms along with their optimization analyses have sh
Externí odkaz:
http://arxiv.org/abs/2409.03863
Autor:
Jamba Team, Lenz, Barak, Arazi, Alan, Bergman, Amir, Manevich, Avshalom, Peleg, Barak, Aviram, Ben, Almagor, Chen, Fridman, Clara, Padnos, Dan, Gissin, Daniel, Jannai, Daniel, Muhlgay, Dor, Zimberg, Dor, Gerber, Edden M, Dolev, Elad, Krakovsky, Eran, Safahi, Erez, Schwartz, Erez, Cohen, Gal, Shachaf, Gal, Rozenblum, Haim, Bata, Hofit, Blass, Ido, Magar, Inbal, Dalmedigos, Itay, Osin, Jhonathan, Fadlon, Julie, Rozman, Maria, Danos, Matan, Gokhman, Michael, Zusman, Mor, Gidron, Naama, Ratner, Nir, Gat, Noam, Rozen, Noam, Fried, Oded, Leshno, Ohad, Antverg, Omer, Abend, Omri, Lieber, Opher, Dagan, Or, Cohavi, Orit, Alon, Raz, Belson, Ro'i, Cohen, Roi, Gilad, Rom, Glozman, Roman, Lev, Shahar, Meirom, Shaked, Delbari, Tal, Ness, Tal, Asida, Tomer, Gal, Tom Ben, Braude, Tom, Pumerantz, Uriya, Cohen, Yehoshua, Belinkov, Yonatan, Globerson, Yuval, Levy, Yuval Peleg, Shoham, Yoav
We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retai
Externí odkaz:
http://arxiv.org/abs/2408.12570