Nothing Special   »   [go: up one dir, main page]

CERN Accelerating science

Αρχική Σελίδα > Αποτελέσματα αναζήτησης: Khattak, Gulrukh
Παραδείγματα αναζήτησης
Σύνθετη Αναζήτηση
Add to Search +
Αναζήτηση συλλογές:
Ταξινόμηση με: Παρουσίαση αποτελεσμάτων: Μορφή αποτελεσμάτων:
CERN Document Server 7 εγγραφές βρέθηκαν  Η έρευνα πήρε 1.28 δευτερόλεπτα. 
1.
Evaluating POWER Architecture for Distributed Training of Generative Adversarial Networks / Hesam, Ahmad (CERN ; Delft Tech. U.) ; Vallecorsa, Sofia (CERN) ; Khattak, Gulrukh (CERN) ; Carminati, Federico (CERN)
The increased availability of High-Performance Computing resources can enable data scientists to deploy and evaluate data-driven approaches, notably in the field of deep learning, at a rapid pace. As deep neural networks become more complex and are ingesting increasingly larger datasets, it becomes unpractical to perform the training phase on single machine instances due to memory constraints, and extremely long training time. [...]
2019 - 9 p. - Published in : 10.1007/978-3-030-34356-9_32
In : High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers, Frankfurt, Germany, 16 - 20 Jun 2019, pp.432-440
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
2.
Evaluating Mixed-Precision Arithmetic for 3D Generative Adversarial Networks to Simulate High Energy Physics Detectors / Ríos, John Osorio (Barcelona, Polytechnic U.) ; Armejach, Adrià (Barcelona, Polytechnic U.) ; Khattak, Gulrukh (CERN) ; Petit, Eric (Unlisted, FR) ; Vallecorsa, Sofia (CERN) ; Casas, Marc (Barcelona, Polytechnic U.)
Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network training. The usage of Mixed Precision (MP) arithmetic with floating-point 32-bit (FP32) and 16-bit half-precision aims at improving memory and floating-point operations throughput, allowing faster training of bigger models. [...]
2020 - 8 p. - Published in : 10.1109/ICMLA51294.2020.00017
In : 2020 19th IEEE International Conference on Machine Learning and Applications, Online, 14 - 17 Dec 2020, pp.49-56
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
3.
Fast simulation of electromagnetic particle showers in high granularity calorimeters / da Rocha, Ricardo Brito (CERN) ; Carminati, Federico (CERN) ; Khattak, Gulrukh (CERN) ; Vallecorsa, Sofia (CERN)
The future need of simulated events by the LHC experiments and their High Luminosity upgrades, is expected to increase by one or two orders of magnitude. As a consequence, research on new fast simulation solutions, including deep Generative Models, is very active and initial results look promising.We have previously reported on a prototype that we have developed, based on 3 dimensional convolutional Generative Adversarial Network, to simulate particle showers in high-granularity calorimeters. [...]
2020 - 7 p. - Published in : EPJ Web Conf. 245 (2020) 02034 Fulltext from publisher: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.02034
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
4.
Generative Adversarial Networks for fast simulation / Carminati, Federico (CERN) ; Khattak, Gulrukh (CERN) ; Loncar, Vladimir (Belgrade, Inst. Phys.) ; Nguyen, Thong Q (Caltech) ; Pierini, Maurizio (CERN) ; Brito Da Rocha, Ricardo (CERN) ; Samaras-Tsakiris, Konstantinos (CERN) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. [...]
IOP, 2020 - 6 p. - Published in : J. Phys.: Conf. Ser. 1525 (2020) 012064 Published fulltext: PDF;
In : 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Saas Fee, Switzerland, 11 - 15 Mar 2019, pp.012064
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
5.
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics / Belayneh, Dawit (U. Chicago (main)) ; Carminati, Federico (CERN) ; Farbin, Amir (Texas U., Arlington (main)) ; Hooberman, Benjamin (Illinois U., Urbana (main)) ; Khattak, Gulrukh (CERN ; Unlisted, PK) ; Liu, Miaoyuan (Fermilab) ; Liu, Junze (Illinois U., Urbana (main)) ; Olivito, Dominick (UC, San Diego (main)) ; Barin Pacela, Vitória (U. Helsinki (main)) ; Pierini, Maurizio (CERN) et al.
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced in high-energy physics collisions. We train neural networks on shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. [...]
arXiv:1912.06794; FERMILAB-PUB-20-448-CMS.- 2020-07-31 - 31 p. - Published in : Eur. Phys. J. C 80 (2020) 688 Article from SCOAP3: scoap3-fulltext - PDF; scoap - PDF; Fulltext: fermilab-pub-20-448-cms - PDF; 1912.06794 - PDF; Fulltext from Publisher: PDF;
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
6.
3D convolutional GAN for fast simulation / Vallecorsa, Sofia (CERN ; Gangneung-Wonju Natl. U.) ; Carminati, Federico (CERN) ; Khattak, Gulrukh (CERN ; Peshawar U.)
Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. [...]
2019 - 8 p. - Published in : EPJ Web Conf. 214 (2019) 02010 Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.02010
Αναλυτική εγγραφή - Παρόμοιες εγγραφές
7.
Recent progress with the top to bottom approach to vectorization in GeantV / Amadio, Guilherme (CERN) ; Ananya ; Apostolakis, John (CERN) ; Bandieramonte, Marilena (CERN ; U. Pittsburgh (main)) ; Behera, Shiba (Bhabha Atomic Res. Ctr.) ; Bhattacharyya, Abhijit (Bhabha Atomic Res. Ctr.) ; Brun, René (CERN) ; Canal, Philippe (Fermilab) ; Carminati, Federico (CERN) ; Cosmo, Gabriele (CERN) et al.
SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. [...]
2019 - 8 p. - Published in : EPJ Web Conf. 214 (2019) 02007 Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.02007
Αναλυτική εγγραφή - Παρόμοιες εγγραφές

Δείτε ακόμη: παρόμοια ονόματα συγγραφέων
2 Khattak, G
1 Khattak, Gul Ruk
12 Khattak, Gul Rukh
Ενδιαφέρεστε να ενημερώνεστε για νέα αποτελέσματα σχετικά με το ερώτημα αυτό;
Δημιουργήστε ένα προσωπικό email alert ή εγγραφείτε στο RSS feed.
Δεν βρήκατε αυτό που αναζητούσατε; Δοκιμάστε την έρευνά σας σε άλλους servers:
Khattak, Gulrukh σε Amazon
Khattak, Gulrukh σε CERN EDMS
Khattak, Gulrukh σε CERN Intranet
Khattak, Gulrukh σε CiteSeer
Khattak, Gulrukh σε Google Books
Khattak, Gulrukh σε Google Scholar
Khattak, Gulrukh σε Google Web
Khattak, Gulrukh σε IEC		Khattak, Gulrukh σε IHS
Khattak, Gulrukh σε INSPIRE
Khattak, Gulrukh σε ISO
Khattak, Gulrukh σε KISS Books/Journals
Khattak, Gulrukh σε KISS Preprints
Khattak, Gulrukh σε NEBIS
Khattak, Gulrukh σε SLAC Library Catalog
Khattak, Gulrukh σε Scirus