Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Panousis, Konstantinos"'
Autor:
Marcos, Diego, van de Vlasakker, Robert, Athanasiadis, Ioannis N., Bonnet, Pierre, Goeau, Hervé, Joly, Alexis, Kissling, W. Daniel, Leblanc, César, van Proosdij, André S. J., Panousis, Konstantinos P.
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait information for even a moderate number of species is a demanding task that ma
Externí odkaz:
http://arxiv.org/abs/2409.17179
Autor:
Voskou, Andreas, Panousis, Konstantinos P., Partaourides, Harris, Tolias, Kyriakos, Chatzis, Sotirios
Publikováno v:
Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023. p. 1966-1975
Automatic Sign Language Translation (SLT) is a research avenue of great societal impact. End-to-End SLT facilitates the interaction of Hard-of-Hearing (HoH) with hearing people, thus improving their social life and opportunities for participation in
Externí odkaz:
http://arxiv.org/abs/2310.04753
Modern deep networks are highly complex and their inferential outcome very hard to interpret. This is a serious obstacle to their transparent deployment in safety-critical or bias-aware applications. This work contributes to post-hoc interpretability
Externí odkaz:
http://arxiv.org/abs/2310.04929
Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. Thi
Externí odkaz:
http://arxiv.org/abs/2310.02116
The recent mass adoption of DNNs, even in safety-critical scenarios, has shifted the focus of the research community towards the creation of inherently intrepretable models. Concept Bottleneck Models (CBMs) constitute a popular approach where hidden
Externí odkaz:
http://arxiv.org/abs/2308.10782
Autor:
Petropoulos, Anastasios, Siakoulis, Vassilis, Panousis, Konstantinos P., Papadoulas, Loukas, Chatzis, Sotirios
In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques. We focus particularly on the US economy but the methodology can be applied also to other economies. Specifi
Externí odkaz:
http://arxiv.org/abs/2301.09856
This work aims to address the long-established problem of learning diversified representations. To this end, we combine information-theoretic arguments with stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA) uni
Externí odkaz:
http://arxiv.org/abs/2201.03624
This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training se
Externí odkaz:
http://arxiv.org/abs/2112.02671
Autor:
Nicolaou, Sergis, Mavrides, Lambros, Tryfou, Georgina, Tolias, Kyriakos, Panousis, Konstantinos, Chatzis, Sotirios, Theodoridis, Sergios
Speech is the most common way humans express their feelings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen treme
Externí odkaz:
http://arxiv.org/abs/2109.07228
Autor:
Voskou, Andreas, Panousis, Konstantinos P., Kosmopoulos, Dimitrios, Metaxas, Dimitris N., Chatzis, Sotirios
Automating sign language translation (SLT) is a challenging real world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the
Externí odkaz:
http://arxiv.org/abs/2109.13318