Zobrazeno 1 - 10
of 1 142
pro vyhledávání: '"Videau, P."'
Recent advancements have highlighted that large language models (LLMs), when given a small set of task-specific examples, demonstrate remarkable proficiency, a capability that extends to complex reasoning tasks. In particular, the combination of few-
Externí odkaz:
http://arxiv.org/abs/2412.04291
Mixture-of-Experts (MoE) models have shown promising potential for parameter-efficient scaling across various domains. However, the implementation in computer vision remains limited, and often requires large-scale datasets comprising billions of samp
Externí odkaz:
http://arxiv.org/abs/2411.18322
Autor:
Videau, Mathurin, Zameshina, Mariia, Leite, Alessandro, Najman, Laurent, Schoenauer, Marc, Teytaud, Olivier
AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying non-differentiable optimization, including evolutionary methods, to refine fully-trained machine learning models by optimizing a set of carefully chosen parameters or hyperp
Externí odkaz:
http://arxiv.org/abs/2410.11330
Energy consumption has become a critical design metric and a limiting factor in the development of future computing architectures, from small wearable devices to large-scale leadership computing facilities. The predominant methods in energy managemen
Externí odkaz:
http://arxiv.org/abs/2410.11855
Autor:
Lai, S., Utehs, J., Wilhahn, A., Fouz, M. C., Bach, O., Brianne, E., Ebrahimi, A., Gadow, K., Göttlicher, P., Hartbrich, O., Heuchel, D., Irles, A., Krüger, K., Kvasnicka, J., Lu, S., Neubüser, C., Provenza, A., Reinecke, M., Sefkow, F., Schuwalow, S., De Silva, M., Sudo, Y., Tran, H. L., Liu, L., Masuda, R., Murata, T., Ootani, W., Seino, T., Takatsu, T., Tsuji, N., Pöschl, R., Richard, F., Zerwas, D., Hummer, F., Simon, F., Boudry, V., Brient, J-C., Nanni, J., Videau, H., Buhmann, E., Garutti, E., Huck, S., Kasieczka, G., Martens, S., Rolph, J., Wellhausen, J., Bilki, B., Northacker, D., Onel, Y., Emberger, L., Graf, C.
To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three publi
Externí odkaz:
http://arxiv.org/abs/2407.00178
Autor:
Lai, S., Utehs, J., Wilhahn, A., Bach, O., Brianne, E., Ebrahimi, A., Gadow, K., Göttlicher, P., Hartbrich, O., Heuchel, D., Irles, A., Krüger, K., Kvasnicka, J., Lu, S., Neubüser, C., Provenza, A., Reinecke, M., Sefkow, F., Schuwalow, S., De Silva, M., Sudo, Y., Tran, H. L., Buhmann, E., Garutti, E., Huck, S., Kasieczka, G., Martens, S., Rolph, J., Wellhausen, J., Blazey, G. C., Dyshkant, A., Francis, K., Zutshi, V., Bilki, B., Northacker, D., Onel, Y., Hummer, F., Simon, F., Kawagoe, K., Onoe, T., Suehara, T., Tsumura, S., Yoshioka, T., Fouz, M. C., Emberger, L., Graf, C., Wagner, M., Pöschl, R., Richard, F., Zerwas, D., Boudry, V., Brient, J-C., Nanni, J., Videau, H., Liu, L., Masuda, R., Murata, T., Ootani, W., Takatsu, T., Tsuji, N., Chadeeva, M., Danilov, M., Korpachev, S., Rusinov, V.
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to
Externí odkaz:
http://arxiv.org/abs/2403.04632
Autor:
Wu, Xingfu, Tramm, John R., Larson, Jeffrey, Navarro, John-Luke, Balaprakash, Prasanna, Videau, Brice, Kruse, Michael, Hovland, Paul, Taylor, Valerie, Hall, Mary
ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations
Externí odkaz:
http://arxiv.org/abs/2402.09222
Autor:
Randall, Thomas, Koo, Jaehoon, Videau, Brice, Kruse, Michael, Wu, Xingfu, Hovland, Paul, Hall, Mary, Ge, Rong, Balaprakash, Prasanna
Publikováno v:
Proceedings of the 37th International Conference on Supercomputing (2023) 37-49
As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increased complexity of these HPC systems and application tuning, empirical pe
Externí odkaz:
http://arxiv.org/abs/2401.04669
Autor:
Wu, Xingfu, Balaprakash, Prasanna, Kruse, Michael, Koo, Jaehoon, Videau, Brice, Hovland, Paul, Taylor, Valerie, Geltz, Brad, Jana, Siddhartha, Hall, Mary
Publikováno v:
to be pushilshed in CUG2023
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to au
Externí odkaz:
http://arxiv.org/abs/2303.16245
Autor:
Trajanov, Risto, Nikolikj, Ana, Cenikj, Gjorgjina, Teytaud, Fabien, Videau, Mathurin, Teytaud, Olivier, Eftimov, Tome, López-Ibáñez, Manuel, Doerr, Carola
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, max
Externí odkaz:
http://arxiv.org/abs/2209.04412