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of 41 437
pro vyhledávání: '"P A, Schroeder"'
Deep Reinforcement learning has shown to be a powerful tool for developing policies in environments where an optimal solution is unclear. In this paper, we attempt to apply Twin Delayed Deep Deterministic Policy Gradients to train a neural network to
Externí odkaz:
http://arxiv.org/abs/2412.14367
Autor:
Kohl, Jens, Gloger, Luisa, Costa, Rui, Kruse, Otto, Luitz, Manuel P., Katz, David, Barbeito, Gonzalo, Schweier, Markus, French, Ryan, Schroeder, Jonas, Riedl, Thomas, Perri, Raphael, Mostafa, Youssef
As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are pr
Externí odkaz:
http://arxiv.org/abs/2412.14215
Autor:
Wang, Yuxin, Schröder, Maresa, Frauen, Dennis, Schweisthal, Jonas, Hess, Konstantin, Feuerriegel, Stefan
Constructing confidence intervals (CIs) for the average treatment effect (ATE) from patient records is crucial to assess the effectiveness and safety of drugs. However, patient records typically come from different hospitals, thus raising the questio
Externí odkaz:
http://arxiv.org/abs/2412.11511
Autor:
Kazemi-Moridani, Amir, Baker, Andrew J., Verheijen, Marc, Gawiser, Eric, Blyth, Sarah-Louise, Obreschkow, Danail, Chemin, Laurent, Collier, Jordan D., Cook, Kyle W., Delhaize, Jacinta, Elson, Ed, Frank, Bradley S., Glowacki, Marcin, Hess, Kelley M., Holwerda, Benne W., Hutchens, Zackary L., Jarvis, Matt J., Kaasinen, Melanie, Makhathini, Sphesihle, Mohapatra, Abhisek, Pan, Hengxing, Schröder, Anja C., Stockenstroom, Leyya, Vaccari, Mattia, Westmeier, Tobias, Wu, John F., Zwaan, Martin
We present measurements of the neutral atomic hydrogen (HI) mass function (HIMF) and cosmic HI density ($\Omega_{\rm HI}$) at $0 \leq z \leq 0.088$ from the Looking at the Distant Universe with MeerKAT Array (LADUMA) survey. Using LADUMA Data Release
Externí odkaz:
http://arxiv.org/abs/2412.11426
Autor:
Kwisthout, Johan, Schroeder, Andrew
The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither obser
Externí odkaz:
http://arxiv.org/abs/2412.09264
Autor:
Abbasi, R., Ackermann, M., Adams, J., Agarwalla, S. K., Aguado, T., Aguilar, J. A., Ahlers, M., Alameddine, J. M., Amin, N. M., Andeen, K., Argüelles, C., Ashida, Y., Athanasiadou, S., Axani, S. N., Babu, R., Bai, X., V., A. Balagopal, Baricevic, M., Barwick, S. W., Bash, S., Basu, V., Bay, R., Beatty, J. J., Tjus, J. Becker, Beise, J., Bellenghi, C., BenZvi, S., Berley, D., Bernardini, E., Besson, D. Z., Blaufuss, E., Bloom, L., Blot, S., Bontempo, F., Motzkin, J. Y. Book, Meneguolo, C. Boscolo, Böser, S., Botner, O., Böttcher, J., Braun, J., Brinson, B., Brisson-Tsavoussis, Z., Brostean-Kaiser, J., Brusa, L., Burley, R. T., Butterfield, D., Campana, M. A., Caracas, I., Carloni, K., Carpio, J., Chattopadhyay, S., Chau, N., Chen, Z., Chirkin, D., Choi, S., Clark, B. A., Cochling, C., Coleman, A., Coleman, P., Collin, G. H., Connolly, A., Conrad, J. M., Corley, R., Cowen, D. F., De Clercq, C., DeLaunay, J. J., Delgado, D., Deng, S., Desai, A., Desiati, P., de Vries, K. D., de Wasseige, G., DeYoung, T., Diaz, A., Díaz-Vélez, J. C., Dierichs, P., Dittmer, M., Domi, A., Draper, L., Dujmovic, H., Durnford, D., Dutta, K., DuVernois, M. A., Ehrhardt, T., Eidenschink, L., Eimer, A., Eller, P., Ellinger, E., Mentawi, S. El, Elsässer, D., Engel, R., Erpenbeck, H., Esmail, W., Evans, J., Evenson, P. A., Fan, K. L., Fang, K., Farrag, K., Fazely, A. R., Fedynitch, A., Feigl, N., Fiedlschuster, S., Finley, C., Fischer, L., Fox, D., Franckowiak, A., Fukami, S., Fürst, P., Gallagher, J., Ganster, E., Garcia, A., Garcia, M., Garg, G., Genton, E., Gerhardt, L., Ghadimi, A., Girard-Carillo, C., Glaser, C., Glüsenkamp, T., Gonzalez, J. G., Goswami, S., Granados, A., Grant, D., Gray, S. J., Griffin, S., Griswold, S., Groth, K. M., Guevel, D., Günther, C., Gutjahr, P., Gruchot, K., Ha, C., Haack, C., Hallgren, A., Halve, L., Halzen, F., Hamacher, L., Hamdaoui, H., Minh, M. Ha, Handt, M., Hanson, K., Hardin, J., Harnisch, A. A., Hatch, P., Haungs, A., Häußler, J., Hardy, A., Hayes, W., Helbing, K., Hellrung, J., Hermannsgabner, J., Heuermann, L., Heyer, N., Hickford, S., Hidvegi, A., Hill, C., Hill, G. C., Hmaid, R., Hoffman, K. D., Hori, S., Hoshina, K., Hostert, M., Hou, W., Huber, T., Hultqvist, K., Hünnefeld, M., Hussain, R., Hymon, K., Ishihara, A., Iwakiri, W., Jacquart, M., Jain, S., Janik, O., Jansson, M., Jeong, M., Jin, M., Jones, B. J. P., Kamp, N., Kang, D., Kang, W., Kang, X., Kappes, A., Kappesser, D., Kardum, L., Karg, T., Karl, M., Karle, A., Katil, A., Katz, U., Kauer, M., Kelley, J. L., Khanal, M., Zathul, A. Khatee, Kheirandish, A., Kiryluk, J., Klein, S. R., Kobayashi, Y., Kochocki, A., Koirala, R., Kolanoski, H., Kontrimas, T., Köpke, L., Kopper, C., Koskinen, D. J., Koundal, P., Kowalski, M., Kozynets, T., Krieger, N., Krishnamoorthi, J., Kruiswijk, K., Krupczak, E., Kumar, A., Kun, E., Kurahashi, N., Lad, N., Gualda, C. Lagunas, Lamoureux, M., Larson, M. J., Lauber, F., Lazar, J. P., Lee, J. W., DeHolton, K. Leonard, Leszczyńska, A., Liao, J., Lincetto, M., Liu, Y. T., Liubarska, M., Love, C., Lu, L., Lucarelli, F., Luszczak, W., Lyu, Y., Madsen, J., Magnus, E., Mahn, K. B. M., Makino, Y., Manao, E., Mancina, S., Mand, A., Sainte, W. Marie, Mariş, I. C., Marka, S., Marka, Z., Marsee, M., Martinez-Soler, I., Maruyama, R., Mayhew, F., McNally, F., Mead, J. V., Meagher, K., Mechbal, S., Medina, A., Meier, M., Merckx, Y., Merten, L., Mitchell, J., Montaruli, T., Moore, R. W., Morii, Y., Morse, R., Moulai, M., Moy, A., Mukherjee, T., Naab, R., Nakos, M., Naumann, U., Necker, J., Negi, A., Neste, L., Neumann, M., Niederhausen, H., Nisa, M. U., Noda, K., Noell, A., Novikov, A., Pollmann, A. Obertacke, O'Dell, V., Olivas, A., Orsoe, R., Osborn, J., O'Sullivan, E., Palusova, V., Pandya, H., Park, N., Parker, G. K., Parrish, V., Paudel, E. N., Paul, L., Heros, C. Pérez de los, Pernice, T., Peterson, J., Pizzuto, A., Plum, M., Pontén, A., Popovych, Y., Rodriguez, M. Prado, Pries, B., Procter-Murphy, R., Przybylski, G. T., Pyras, L., Raab, C., Rack-Helleis, J., Rad, N., Ravn, M., Rawlins, K., Rechav, Z., Rehman, A., Resconi, E., Reusch, S., Rhode, W., Riedel, B., Rifaie, A., Roberts, E. J., Robertson, S., Rodan, S., Roellinghoff, G., Rongen, M., Rosted, A., Rott, C., Ruhe, T., Ruohan, L., Ryckbosch, D., Safa, I., Saffer, J., Salazar-Gallegos, D., Sampathkumar, P., Sandrock, A., Santander, M., Sarkar, S., Savelberg, J., Savina, P., Schaile, P., Schaufel, M., Schieler, H., Schindler, S., Schlickmann, L., Schlüter, B., Schlüter, F., Schmeisser, N., Schmidt, E., Schmidt, T., Schneider, J., Schröder, F. G., Schumacher, L., Schwirn, S., Sclafani, S., Seckel, D., Seen, L., Seikh, M., Seo, M., Seunarine, S., Myhr, P. Sevle, Shah, R., Shefali, S., Shimizu, N., Silva, M., Simmons, A., Skrzypek, B., Smithers, B., Snihur, R., Soedingrekso, J., Søgaard, A., Soldin, D., Soldin, P., Sommani, G., Spannfellner, C., Spiczak, G. M., Spiering, C., Stachurska, J., Stamatikos, M., Stanev, T., Stezelberger, T., Stürwald, T., Stuttard, T., Sullivan, G. W., Taboada, I., Ter-Antonyan, S., Terliuk, A., Thiesmeyer, M., Thompson, W. G., Thorpe, A., Thwaites, J., Tilav, S., Tollefson, K., Tönnis, C., Toscano, S., Tosi, D., Trettin, A., Turcotte, R., Elorrieta, M. A. Unland, Upadhyay, A. K., Upshaw, K., Vaidyanathan, A., Valtonen-Mattila, N., Vandenbroucke, J., van Eijndhoven, N., Vannerom, D., van Santen, J., Vara, J., Varsi, F., Veitch-Michaelis, J., Venugopal, M., Vereecken, M., Carrasco, S. Vergara, Verpoest, S., Veske, D., Vijai, A., Walck, C., Wang, A., Weaver, C., Weigel, P., Weindl, A., Weldert, J., Wen, A. Y., Wendt, C., Werthebach, J., Weyrauch, M., Whitehorn, N., Wiebusch, C. H., Williams, D. R., Witthaus, L., Wolf, M., Woodward, H., Wrede, G., Xu, X. W., Yanez, J. P., Yildizci, E., Yoshida, S., Young, R., Yu, S., Yuan, T., Zegarelli, A., Zhang, S., Zhang, Z., Zhelnin, P., Zilberman, P., Zimmerman, M.
We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosm
Externí odkaz:
http://arxiv.org/abs/2412.05046
Autor:
Motwani, Sumeet Ramesh, Smith, Chandler, Das, Rocktim Jyoti, Rybchuk, Markian, Torr, Philip H. S., Laptev, Ivan, Pizzati, Fabio, Clark, Ronald, de Witt, Christian Schroeder
Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. While LLMs are typically used as single-model generators, where humans critique and refine their outputs, the pote
Externí odkaz:
http://arxiv.org/abs/2412.01928
Autor:
Mineault, Patrick, Zanichelli, Niccolò, Peng, Joanne Zichen, Arkhipov, Anton, Bingham, Eli, Jara-Ettinger, Julian, Mackevicius, Emily, Marblestone, Adam, Mattar, Marcelo, Payne, Andrew, Sanborn, Sophia, Schroeder, Karen, Tavares, Zenna, Tolias, Andreas
As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general intelligence, they perform robustly even under conditions that deviat
Externí odkaz:
http://arxiv.org/abs/2411.18526
Autor:
Boelts, Jan, Deistler, Michael, Gloeckler, Manuel, Tejero-Cantero, Álvaro, Lueckmann, Jan-Matthis, Moss, Guy, Steinbach, Peter, Moreau, Thomas, Muratore, Fabio, Linhart, Julia, Durkan, Conor, Vetter, Julius, Miller, Benjamin Kurt, Herold, Maternus, Ziaeemehr, Abolfazl, Pals, Matthijs, Gruner, Theo, Bischoff, Sebastian, Krouglova, Nastya, Gao, Richard, Lappalainen, Janne K., Mucsányi, Bálint, Pei, Felix, Schulz, Auguste, Stefanidi, Zinovia, Rodrigues, Pedro, Schröder, Cornelius, Zaid, Faried Abu, Beck, Jonas, Kapoor, Jaivardhan, Greenberg, David S., Gonçalves, Pedro J., Macke, Jakob H.
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this
Externí odkaz:
http://arxiv.org/abs/2411.17337
Autor:
Li, R., Picksley, A., Benedetti, C., Filippi, F., Stackhouse, J., Fan-Chiang, L., Tsai, H. E., Nakamura, K., Schroeder, C. B., van Tilborg, J., Esarey, E., Geddes, C. G. R., Gonsalves, A. J.
Modern laser plasma accelerators (LPAs) often require meter-scale plasma waveguides to propagate a high-intensity drive laser pulse. Tapering the longitudinal gas density profile in meter-scale gas jets could allow for single stage laser plasma accel
Externí odkaz:
http://arxiv.org/abs/2411.17028