no code implementations • 7 Oct 2023 • Andreas Voskou, Konstantinos P. Panousis, Harris Partaourides, Kyriakos Tolias, Sotirios Chatzis
A characteristic example is Phoenix2014T benchmark dataset, which only covers weather forecasts in German Sign Language.
no code implementations • 3 Oct 2023 • Konstantinos P. Panousis, Dino Ienco, Diego Marcos
Deep Learning algorithms have recently gained significant attention due to their impressive performance.
1 code implementation • 21 Aug 2023 • Konstantinos P. Panousis, Dino Ienco, Diego Marcos
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.
no code implementations • 24 Jan 2023 • Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis, Loukas Papadoulas, Sotirios Chatzis
In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques.
no code implementations • 10 Jan 2022 • Konstantinos P. Panousis, Anastasios Antoniadis, Sotirios Chatzis
To this end, we combine information-theoretic arguments with stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA) units.
1 code implementation • 5 Dec 2021 • Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
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 settings.
Ranked #2 on Adversarial Robustness on CIFAR-10
1 code implementation • ICCV 2021 • Andreas Voskou, Konstantinos P. Panousis, Dimitrios Kosmopoulos, Dimitris N. Metaxas, Sotirios Chatzis
In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth.
no code implementations • 4 Jan 2021 • Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, Sergios Theodoridis
The main operating principle of the introduced units lies on stochastic arguments, as the network performs posterior sampling over competing units to select the winner.
no code implementations • 23 Sep 2020 • Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis, Loukas Papadoulas, Sotirios Chatzis
Current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models.
no code implementations • 18 Jun 2020 • Antonios Alexos, Konstantinos P. Panousis, Sotirios Chatzis
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments.
no code implementations • 13 Feb 2020 • Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general.
1 code implementation • 19 May 2018 • Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition.