Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Klampanos Iraklis"'
Autor:
Troumpoukis, Antonis, Klampanos, Iraklis, Pantazi, Despina-Athanasia, Albughdadi, Mohanad, Baousis, Vasileios, Barrilero, Omar, Bojor, Alexandra, Branco, Pedro, Bruzzone, Lorenzo, Chietera, Andreina, Fournand, Philippe, Hall, Richard, Lazzarini, Michele, Luna, Adrian, Nousias, Alexandros, Perentis, Christos, Petrakis, George, Punjani, Dharmen, Röbl, David, Stamoulis, George, Tsalapati, Eleni, Urbanavičiūtė, Indrė, Weikmann, Giulio, Ziouvelou, Xenia, Ziółkowski, Marcin, Koubarakis, Manolis, Karkaletsis, Vangelis
Publikováno v:
In Future Generation Computer Systems November 2024 160:505-521
Learning disentangled representations without supervision or inductive biases, often leads to non-interpretable or undesirable representations. On the other hand, strict supervision requires detailed knowledge of the true generative factors, which is
Externí odkaz:
http://arxiv.org/abs/2008.09879
When observing a phenomenon, severe cases or anomalies are often characterised by deviation from the expected data distribution. However, non-deviating data samples may also implicitly lead to severe outcomes. In the case of unsupervised severe weath
Externí odkaz:
http://arxiv.org/abs/2005.07243
Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data
Externí odkaz:
http://arxiv.org/abs/1912.10490
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome.
Externí odkaz:
http://arxiv.org/abs/1811.03909
Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly diffic
Externí odkaz:
http://arxiv.org/abs/1804.02528
Autor:
Grossman, Robert L., Greenway, Matthew, Heath, Allison P., Powell, Ray, Suarez, Rafael D., Wells, Walt, White, Kevin, Atkinson, Malcolm, Klampanos, Iraklis, Alvarez, Heidi L., Harvey, Christine, Mambretti, Joe J.
In this paper we describe the design, and implementation of the Open Science Data Cloud, or OSDC. The goal of the OSDC is to provide petabyte-scale data cloud infrastructure and related services for scientists working with large quantities of data. C
Externí odkaz:
http://arxiv.org/abs/1601.00323
Autor:
Politikos, Dimitris V., Fakiris, Elias, Davvetas, Athanasios, Klampanos, Iraklis A., Papatheodorou, George
Publikováno v:
In Marine Pollution Bulletin March 2021 164
Autor:
Atkinson, Malcolm, Carpené, Michele, Casarotti, Emanuele, Claus, Steffen, Filgueira, Rosa, Frank, Anton, Galea, Michelle, Garth, Tom, Gemünd, André, Igel, Heiner, Klampanos, Iraklis, Krause, Amrey, Krischer, Lion, Leong, Siew Hoon, Magnoni, Federica, Matser, Jonas, Michelini, Alberto, Rietbrock, Andreas, Schwichtenberg, Horst, Spinuso, Alessandro, Vilotte, Jean-Pierre
The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE scien
Externí odkaz:
http://arxiv.org/abs/1510.01989
Autor:
Klampanos, Iraklis A., Davvetas, Athanasios, Andronopoulos, Spyros, Pappas, Charalambos, Ikonomopoulos, Andreas, Karkaletsis, Vangelis
Publikováno v:
In Environmental Modelling and Software April 2018 102:84-93