3 citations · 8 across the 14 of their papers we have counts for
16 papers
Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models
Konstantinos P. Panousis, Diego Marcos
The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability. Despite strong performance, these models are often treated as bl…
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis +7
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait informa…
DISCOVER: Making Vision Networks Interpretable via Competition and Dissection
Konstantinos P. Panousis, Sotirios Chatzis
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-…
A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset
Andreas Voskou, Konstantinos P. Panousis, Harris Partaourides +2
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, th…
Coarse-to-Fine Concept Bottleneck Models
Konstantinos P. Panousis, Dino Ienco, Diego Marcos
Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinde…
Sparse Linear Concept Discovery Models
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. Concep…
Macroeconomic forecasting and sovereign risk assessment using deep learning techniques
Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis +2
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 econo…
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations
Konstantinos P. Panousis, Anastasios Antoniadis, Sotirios Chatzis
This work aims to address the long-established problem of learning diversified representations. To this end, we combine information-theoretic arguments with stochastic competition-…
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
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-b…
Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation
Andreas Voskou, Konstantinos P. Panousis, Dimitrios Kosmopoulos +2
Automating sign language translation (SLT) is a challenging real world application. Despite its societal importance, though, research progress in the field remains rather poor. Cru…
Dialog speech sentiment classification for imbalanced datasets
Sergis Nicolaou, Lambros Mavrides, Georgina Tryfou +4
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…
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos +1
This work addresses adversarial robustness in deep learning by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units resul…
A Deep Learning Approach for Dynamic Balance Sheet Stress Testing
Anastasios Petropoulos, Vassilis Siakoulis, Konstantinos P. Panousis +2
In the aftermath of the financial crisis, supervisory authorities have considerably altered the mode of operation of financial stress testing. Despite these efforts, significant co…
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
Antonios Alexos, Konstantinos P. Panousis, Sotirios Chatzis
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments. Specifically, inspired from results in neuroscience, we propose a local…
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition
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. However, the commonly employed assumption of the depend…
Nonparametric Bayesian Deep Networks with Local Competition
Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inferen…