Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Alexandros Koliousis"'
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-12 (2024)
Abstract This paper uses computational methods to simultaneously investigate the epistemological effects of misinformation on communities of rational agents, while also contributing to the philosophical debate on ‘higher-order’ evidence (i.e. evi
Externí odkaz:
https://doaj.org/article/ee77f37adb9742a9b4d0452c625b7654
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-9 (2024)
Abstract In this paper, we situate our computational approach to philosophy relative to other digital humanities and computational social science practices, based on reflections stemming from our research on the PolyGraphs project in social epistemol
Externí odkaz:
https://doaj.org/article/cf0f4eb318114d6a854f5e2243a26791
Autor:
Brian Ball, Alexandros Koliousis
Publikováno v:
AI & SOCIETY. 38:861-868
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d3dc3463958b3b855791a19e528ae73
https://doi.org/10.1007/s00146-022-01535-7
https://doi.org/10.1007/s00146-022-01535-7
Publikováno v:
ACM SIGOPS Operating Systems Review. 53:52-58
Deep learning (DL) systems expose many tuning parameters ("hyper-parameters") that affect the performance and accuracy of trained models. Increasingly users struggle to configure hyper-parameters, and a substantial portion of time is spent tuning the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96023d72e4a0641c49afa5d583e405d6
https://doi.org/10.1145/3352020.3352029
https://doi.org/10.1145/3352020.3352029
Publikováno v:
SIGMOD Conference
Window aggregation queries are a core part of streaming applications. To support window aggregation efficiently, stream processing engines face a trade-off between exploiting parallelism (at the instruction/multi-core levels) and incremental computat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9a5c87a80d933152574a1ca8542642a
https://doi.org/10.1145/3318464.3389753
https://doi.org/10.1145/3318464.3389753
Autor:
Paolo Costa, Pijika Watcharapichat, Luo Mai, Alexandros Koliousis, Matthias Weidlich, Peter Pietzuch
Publikováno v:
Very Large Databases (VLDB) 2019
Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::423ceb07533f73294550b4fe38ffa657
http://hdl.handle.net/10044/1/75907
http://hdl.handle.net/10044/1/75907
Autor:
Anca Iordache, John McGlone, Guillaume Pierre, Mark Stillwell, Katerina Argyraki, Peter Sanders, George Ioannidis, Christoph Kleineweber, Jose G. F. Coutinho, Alexander L. Wolf, Thorsten Schütt, Carmelo Ragusa, Alexandros Koliousis, Teng Yu
Publikováno v:
Software Architecture for Big Data and the Cloud
Ivan Mistrik; Rami Bahsoon; Nour Ali; Maritta Heisel; Bruce Maxim. Software Architecture for Big Data and the Cloud, Morgan Kaufmann, 2017, 9780128054673
Ivan Mistrik; Rami Bahsoon; Nour Ali; Maritta Heisel; Bruce Maxim. Software Architecture for Big Data and the Cloud, Morgan Kaufmann, 2017, 9780128054673
International audience; HARNESS is a next generation cloud-computing platform that offers commodity and specialized resources in support of large-scale data processing applications. We focus primarily on application domains that are currently not wel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8238a391726539ac345067f4a1f6b5ff
https://inria.hal.science/hal-01507344/file/harness.pdf
https://inria.hal.science/hal-01507344/file/harness.pdf
Autor:
Nour Ali, Katerina Argyraki, Ioannis N. Athanasiadis, Cigdem Avci Salma, Rami Bahsoon, Alistair Barros, Stephen Bonner, John Brennan, Rajkumar Buyya, Bo Chen, Tao Chen, Mandy Chessell, Jose G.F. Coutinho, Reza Curtmola, Jun Dai, Anastasija Efremovska, Robert Eikermann, Colin Fidge, Andrei Furda, Saurabh Garg, Ian Gorton, Wilhelm Hasselbring, Robert Heinrich, Maritta Heisel, George Ioannidis, Anca Iordache, Reiner Jung, Christoph Kleineweber, Alexandros Koliousis, Ibad Kureshi, Patricia Lago, Markus Look, Darshan Lopes, Bruce Maxim, John McGlone, Ivan Mistrik, Alp Oral, Fiona O'Sullivan, Kevin Palmer, Guillaume Pierre, Deepak Poola, Carlos Queiroz, Carmelo Ragusa, Kotagiri Ramamohanarao, Ralf Reussner, Maria A. Rodriguez, Alexander Roth, Berhard Rumpe, Mark Ryan, Mohsen Amini Salehi, Peter Sanders, Thorsten Schütt, Mark Stillwell, Bedir Tekinerdogan, Georgios Theodoropoulos, Tim Vincent, Alexander Wolf, Dan Wolfson, Andreas Wortmann, Jiangshan Yu, Teng Yu, Xinwei Zhao, Olaf Zimmermann, Christian Zirkelbach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a08e7d9bbf79ad6d70f02c0c11519499
https://doi.org/10.1016/b978-0-12-805467-3.00024-7
https://doi.org/10.1016/b978-0-12-805467-3.00024-7
Autor:
Paolo Costa, Peter Pietzuch, Alexander L. Wolf, Matthias Weidlich, Alexandros Koliousis, Raul Fernandez
Publikováno v:
SIGMOD Conference
2016 ACM SIGMOD/PODS Conference
2016 ACM SIGMOD/PODS Conference
Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supporte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ce03fc4e3c0be256abb33428bfcd816
https://doi.org/10.1145/2882903.2882906
https://doi.org/10.1145/2882903.2882906
Autor:
Raul Fernandez, Matthias Weidlich, Alexandros Koliousis, Peter Pietzuch, Paolo Costa, Alexander L. Wolf
Publikováno v:
DEBS
Heterogeneous architectures that combine multi-core CPUs with many-core GPGPUs have the potential to improve the performance of data-intensive stream processing applications. Yet, a stream processing engine must execute streaming SQL queries with suf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9c4b311c15b47ea182752ef7b7d7910
https://doi.org/10.1145/2933267.2933291
https://doi.org/10.1145/2933267.2933291