Zobrazeno 1 - 10
of 60 262
pro vyhledávání: '"Mahoney, A."'
In this work, we consider solving optimization problems with a stochastic objective and deterministic equality constraints. We propose a Trust-Region Sequential Quadratic Programming method to find both first- and second-order stationary points. Our
Externí odkaz:
http://arxiv.org/abs/2409.15734
Autor:
Dcruz, Julian Gerald, Mahoney, Sam, Chua, Jia Yun, Soukhabandith, Adoundeth, Mugabe, John, Guo, Weisi, Arana-Catania, Miguel
Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancin
Externí odkaz:
http://arxiv.org/abs/2409.13423
Consensus planning is a method for coordinating decision making across complex systems and organizations, including complex supply chain optimization pipelines. It arises when large interdependent distributed agents (systems) share common resources a
Externí odkaz:
http://arxiv.org/abs/2408.16462
Autor:
Adderley, P. A., Ahmed, S., Allison, T., Bachimanchi, R., Baggett, K., BastaniNejad, M., Bevins, B., Bevins, M., Bickley, M., Bodenstein, R. M., Bogacz, S. A., Bruker, M., Burrill, A., Cardman, L., Creel, J., Chao, Y. -C., Cheng, G., Ciovati, G., Chattopadhyay, S., Clark, J., Clemens, W. A., Croke, G., Daly, E., Davis, G. K., Delayen, J., De Silva, S. U., Dickson, R., Diaz, M., Drury, M., Doolittle, L., Douglas, D., Feldl, E., Fischer, J., Freyberger, A., Ganni, V., Geng, R. L., Ginsburg, C., Gomez, J., Grames, J., Gubeli, J., Guo, J., Hannon, F., Hansknecht, J., Harwood, L., Henry, J., Hernandez-Garcia, C., Higgins, S., Higinbotham, D., Hofler, A. S., Hiatt, T., Hogan, J., Hovater, C., Hutton, A., Jones, C., Jordan, K., Joyce, M., Kazimi, R., Keesee, M., Kelley, M. J., Keppel, C., Kimber, A., King, L., Kjeldsen, P., Kneisel, P., Koval, J., Krafft, G. A., Lahti, G., Larrieu, T., Lauze, R., Leemann, C., Legg, R., Li, R., Lin, F., Machie, D., Mammosser, J., Macha, K., Mahoney, K., Marhauser, F., Mastracci, B., Matalevich, J., McCarter, J., McCaughan, M., Merminga, L., Michaud, R., Morozov, V., Mounts, C., Musson, J., Nelson, R., Oren, W., Overton, R. B., Palacios-Serrano, G., Park, H. -K., Phillips, L., Philip, S., Pilat, F., Plawski, T., Poelker, M., Powers, P., Powers, T., Preble, J., Reilly, T., Rimmer, R., Reece, C., Robertson, H., Roblin, Y., Rode, C., Satogata, T., Seidman, D. J., Seryi, A., Shabalina, A., Shin, I., Slominski, R., Slominski, C., Spata, M., Spell, D., Spradlin, J., Stirbet, M., Stutzman, M. L., Suhring, S., Surles-Law, K., Suleiman, R., Tennant, C., Tian, H., Turner, D., Tiefenback, M., Trofimova, O., Valente, A. -M., Wang, H., Wang, Y., White, K., Whitlatch, C., Whitlatch, T., Wiseman, M., Wissman, M. J., Wu, G., Yang, S., Yunn, B., Zhang, S., Zhang, Y.
Publikováno v:
Phys. Rev. Accel. Beams 27 (2024) 084802
This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an a
Externí odkaz:
http://arxiv.org/abs/2408.16880
Autor:
Ren, Pu, Nakata, Rie, Lacour, Maxime, Naiman, Ilan, Nakata, Nori, Song, Jialin, Bi, Zhengfa, Malik, Osman Asif, Morozov, Dmitriy, Azencot, Omri, Erichson, N. Benjamin, Mahoney, Michael W.
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake l
Externí odkaz:
http://arxiv.org/abs/2407.15089
Autor:
Karlbauer, Matthias, Maddix, Danielle C., Ansari, Abdul Fatir, Han, Boran, Gupta, Gaurav, Wang, Yuyang, Stuart, Andrew, Mahoney, Michael W.
Remarkable progress in the development of Deep Learning Weather Prediction (DLWP) models positions them to become competitive with traditional numerical weather prediction (NWP) models. Indeed, a wide number of DLWP architectures -- based on various
Externí odkaz:
http://arxiv.org/abs/2407.14129
Autor:
Lu, Haiquan, Liu, Xiaotian, Zhou, Yefan, Li, Qunli, Keutzer, Kurt, Mahoney, Michael W., Yan, Yujun, Yang, Huanrui, Yang, Yaoqing
Recent studies on deep ensembles have identified the sharpness of the local minima of individual learners and the diversity of the ensemble members as key factors in improving test-time performance. Building on this, our study investigates the interp
Externí odkaz:
http://arxiv.org/abs/2407.12996
Autor:
Baldi, Tommaso, Campos, Javier, Hawks, Ben, Ngadiuba, Jennifer, Tran, Nhan, Diaz, Daniel, Duarte, Javier, Kastner, Ryan, Meza, Andres, Quinnan, Melissa, Weng, Olivia, Geniesse, Caleb, Gholami, Amir, Mahoney, Michael W., Loncar, Vladimir, Harris, Philip, Agar, Joshua, Qin, Shuyu
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for
Externí odkaz:
http://arxiv.org/abs/2406.19522
Large matrices arise in many machine learning and data analysis applications, including as representations of datasets, graphs, model weights, and first and second-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is an area which uses
Externí odkaz:
http://arxiv.org/abs/2406.11151
Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models. However, previous work has either faced limitations when processing larger networks or was task-specif
Externí odkaz:
http://arxiv.org/abs/2406.09997