Zobrazeno 1 - 10
of 309 468
pro vyhledávání: '"A A Harrison"'
Autor:
Brunzema, Paul, Jordahn, Mikkel, Willes, John, Trimpe, Sebastian, Snoek, Jasper, Harrison, James
Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily captured (such as those defined by
Externí odkaz:
http://arxiv.org/abs/2412.09477
Autor:
Abdin, Marah, Aneja, Jyoti, Behl, Harkirat, Bubeck, Sébastien, Eldan, Ronen, Gunasekar, Suriya, Harrison, Michael, Hewett, Russell J., Javaheripi, Mojan, Kauffmann, Piero, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Liu, Weishung, Mendes, Caio C. T., Nguyen, Anh, Price, Eric, de Rosa, Gustavo, Saarikivi, Olli, Salim, Adil, Shah, Shital, Wang, Xin, Ward, Rachel, Wu, Yue, Yu, Dingli, Zhang, Cyril, Zhang, Yi
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code
Externí odkaz:
http://arxiv.org/abs/2412.08905
Autor:
Mizzi, Christopher A., Phelan, W. Adam, Cook, Matthew S., Chappell, Greta L., Tobash, Paul H., Arellano, David C., Prada, Derek V., Maiorov, Boris, Harrison, Neil
The quasiharmonic approximation is the most common method for modeling the specific heat of solids; however, it fails to capture the effects of intrinsic anharmonicity in phonons. In this study, we introduce the "elastic softening approximation," an
Externí odkaz:
http://arxiv.org/abs/2412.07857
Autor:
Bocquet, S., Grandis, S., Krause, E., To, C., Bleem, L. E., Klein, M., Mohr, J. J., Schrabback, T., Alarcon, A., Alves, O., Amon, A., Andrade-Oliveira, F., Baxter, E. J., Bechtol, K., Becker, M. R., Bernstein, G. M., Blazek, J., Camacho, H., Campos, A., Rosell, A. Carnero, Kind, M. Carrasco, Cawthon, R., Chang, C., Chen, R., Choi, A., Cordero, J., Crocce, M., Davis, C., DeRose, J., Diehl, H. T., Dodelson, S., Doux, C., Drlica-Wagner, A., Eckert, K., Eifler, T. F., Elsner, F., Elvin-Poole, J., Everett, S., Fang, X., Ferté, A., Fosalba, P., Friedrich, O., Frieman, J., Gatti, M., Giannini, G., Gruen, D., Gruendl, R. A., Harrison, I., Hartley, W. G., Herner, K., Huang, H., Huff, E. M., Huterer, D., Jarvis, M., Kuropatkin, N., Leget, P. -F., Lemos, P., Liddle, A. R., MacCrann, N., McCullough, J., Muir, J., Myles, J., Navarro-Alsina, A., Pandey, S., Park, Y., Porredon, A., Prat, J., Raveri, M., Rollins, R. P., Roodman, A., Rosenfeld, R., Rykoff, E. S., Sánchez, C., Sanchez, J., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Troxel, M. A., Tutusaus, I., Varga, T. N., Weaverdyck, N., Wechsler, R. H., Wu, H. -Y., Yanny, B., Yin, B., Zhang, Y., Zuntz, J., Abbott, T. M. C., Ade, P. A. R., Aguena, M., Allam, S., Allen, S. W., Anderson, A. J., Ansarinejad, B., Austermann, J. E., Bayliss, M., Beall, J. A., Bender, A. N., Benson, B. A., Bianchini, F., Brodwin, M., Brooks, D., Bryant, L., Burke, D. L., Canning, R. E. A., Carlstrom, J. E., Carretero, J., Castander, F. J., Chang, C. L., Chaubal, P., Chiang, H. C., Chou, T-L., Citron, R., Moran, C. Corbett, Costanzi, M., Crawford, T. M., Crites, A. T., da Costa, L. N., Pereira, M. E. S., Davis, T. M., de Haan, T., Dobbs, M. A., Doel, P., Everett, W., Farahi, A., Flaugher, B., Flores, A. M., Floyd, B., Gallicchio, J., Gaztanaga, E., George, E. M., Gladders, M. D., Gupta, N., Gutierrez, G., Halverson, N. W., Hinton, S. R., Hlavacek-Larrondo, J., Holder, G. P., Hollowood, D. L., Holzapfel, W. L., Hrubes, J. D., Huang, N., Hubmayr, J., Irwin, K. D., James, D. J., Kéruzoré, F., Khullar, G., Kim, K., Knox, L., Kraft, R., Kuehn, K., Lahav, O., Lee, A. T., Lee, S., Li, D., Lidman, C., Lima, M., Lowitz, A., Mahler, G., Mantz, A., Marshall, J. L., McDonald, M., McMahon, J. J., Mena-Fernández, J., Meyer, S. S., Miquel, R., Montgomery, J., Natoli, T., Nibarger, J. P., Noble, G. I., Novosad, V., Ogando, R. L. C., Padin, S., Paschos, P., Patil, S., Malagón, A. A. Plazas, Pryke, C., Reichardt, C. L., Roberson, J., Romer, A. K., Romero, C., Ruhl, J. E., Saliwanchik, B. R., Salvati, L., Samuroff, S., Sanchez, E., Santiago, B., Sarkar, A., Saro, A., Schaffer, K. K., Sharon, K., Sievers, C., Smecher, G., Smith, M., Somboonpanyakul, T., Sommer, M., Stalder, B., Stark, A. A., Stephen, J., Strazzullo, V., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Tucker, C., Tucker, D. L., Veach, T., Vieira, J. D., von der Linden, A., Wang, G., Whitehorn, N., Wu, W. L. K., Yefremenko, V., Young, M., Zebrowski, J. A., Zohren, H., Collaboration, DES, Collaboration, SPT
Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest an
Externí odkaz:
http://arxiv.org/abs/2412.07765
We consider two approaches to designing fermion-qubit mappings: (1) ternary tree transformations, which use Pauli representations of the Majorana operators that correspond to root-to-leaf paths of a tree graph and (2) linear encodings of the Fock bas
Externí odkaz:
http://arxiv.org/abs/2412.07578
Side channels have become an essential component of many modern information-theoretic schemes. The emerging field of cross technology communications (CTC) provides practical methods for creating intentional side channels between existing communicatio
Externí odkaz:
http://arxiv.org/abs/2412.05249
Autor:
Williams, Devin J., Damjanov, Ivana, Sawicki, Marcin, Souchereau, Harrison, Chen, Lingjian, Desprez, Guillaume, George, Angelo, Annunziatella, Marianna, Gwyn, Stephen
Galaxies are predicted to assemble their stellar haloes through the accretion of stellar material from interactions with their cosmic environment. Observations that trace stellar halo buildup probe the processes that drive galaxy size and stellar mas
Externí odkaz:
http://arxiv.org/abs/2412.03662
This project investigates factors that influence the perceived helpfulness of Amazon product reviews through machine learning techniques. After extensive feature analysis and correlation testing, we identified key metadata characteristics that serve
Externí odkaz:
http://arxiv.org/abs/2412.02884
Autor:
Pang, Bo, Cheng, Sibo, Huang, Yuhan, Jin, Yufang, Guo, Yike, Prentice, I. Colin, Harrison, Sandy P., Arcucci, Rossella
Publikováno v:
Computers & Geosciences, Volume 195, 2025, 105783
Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in pre
Externí odkaz:
http://arxiv.org/abs/2412.01400
Estimating the probability of failure is a critical step in developing safety-critical autonomous systems. Direct estimation methods such as Monte Carlo sampling are often impractical due to the rarity of failures in these systems. Existing importanc
Externí odkaz:
http://arxiv.org/abs/2412.02154