PUBLICATIONS

Recurrent neural network pruning using dynamical systems and iterative fine-tuning

Christos Chatzikonstantinou, Dimitrios Konstantinidis, Kosmas Dimitropoulos and Petros Daras.   Network pruning techniques are widely employed to reduce the memory requirements and increase the inference speed of neural networks. This work proposes a novel RNN pruning method that considers the RNN weight matrices as collections of time-evolving signals. Such signals that represent weight vectors can …

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NAct: The Nutrition and Activity Ontology

D. Tsatsou, Elena Lalama, S. L. Wilson-Barnes, K. Hart, V. Cornelissen, R. Buys, I. Pagkalos, S. Balula Dias, K. Dimitropoulos, P. Daras. NAct: The Nutrition & Activity Ontology for healthy living. Formal Ontology in Information Systems: Proceedings of the 12th International Conference (FOIS 2021). IOS Press, 2021.   The Nutrition and Activity Ontology (NAct) is …

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Personalised nutrition for healthy living: The PROTEIN project

S. Wilson-Barnes, L. P. Gymnopoulos, K. Dimitropoulos, V. Solachidis, K. Rouskas, D. Russell, Y. Oikonomidis, S. Hadjidimitriou, J. María Botana, B. Brkic, E. Mantovani, S. Gravina, G. Telo, E. Lalama, R. Buys, M. Hassapidou, S. Balula Dias, A. Batista, L. Perone, S. Bryant, S. Maas, S. Cobello, P. Bacelar, S. A. Lanham-New and K. Hart. …

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Noise-Assisted Multivariate variational mode decomposition

Charilaos A. Zisou, Georgios K. Apostolidis, Leontios J. Hadjileontiadis The variational mode decomposition (VMD) is a widely applied optimization-based method, which analyzes nonstationary signals concurrently. Correspondingly, its recently proposed multivariate extension, i.e., MVMD, has shown great potentials in analyzing multichannel signals. However, the requirement of presetting the number of extracted components K diminishes the analytic …

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A Deep Network for Automatic Video-Based Food Bite Detection

Dimitrios Konstantinidis, Kosmas Dimitropoulos, Ioannis Ioakimidis, Billy Lang-Le, Petros Daras Past research has now provided compelling evidence pointing towards correlations among individual eating styles and the development of (un)healthy eating patterns, obesity and other medical conditions. In this setting, an automatic, non-invasive food bite detection system can be a really useful tool in the hands …

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A Cross-Modal Variational Framework For Food Image Analysis

T. Theodoridis, V. Solachidis, K. Dimitropoulso, and P. Daras   Food analysis resides at the core of modern nutrition recommender systems, providing the foundation for a high-level understanding of users’ eating habits. This paper focuses on the sub-task of ingredient recognition from food images using a variational framework. The framework consists of two variational encoder-decoder …

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Single Image-Based Food Volume Estimation Using Monocular Depth-Prediction Networks

Graikos A., Charisis V., Iakovakis D., Hadjidimitriou S., Hadjileontiadis L. (2020) Single Image-Based Food Volume Estimation Using Monocular Depth-Prediction Networks. In: Antona M., Stephanidis C.   In this work, we present a system that can estimate food volume from a single input image, by utilizing the latest advancements in monocular depth estimation. We employ a …

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A survey on AI nutrition recommender systems

Thomas Theodoridis, Vassilios Solachidis, Kosmas Dimitropoulos, Lazaros Gymnopoulos, and Petros Daras   The goal of this work is to provide an overview of existing approaches regarding AI nutrition recommender systems. A breakdown of such systems into task-specific components is presented, as well as methodologies concerned with each individual component. The components of an idealized AI …

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