65 citations · 333 across the 34 of their papers we have counts for
39 papers
BMASH: Ball-Motion-Aware Soccer Header Spotting
Ahmed Endris Hasen, Muhammad Shahzad Khan, Nikolaos Passalis +1
Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety applications. In soccer, howe…
MS-RFD: Multi-Signal Release Frame Detection in Hammer Throw from Reconstructed 3D Trajectories
Ahmed Endris Hasen, Nikolaos Passalis, Tomi Vanttinen +1
Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and performance analysis. In ham…
A 262 TOPS Hyperdimensional Photonic AI Accelerator powered by a Si3N4 microcomb laser
Christos Pappas, Antonios Prapas, Theodoros Moschos +13
The ever-increasing volume of data has necessitated a new computing paradigm, embodied through Artificial Intelligence (AI) and Large Language Models (LLMs). Digital electronic AI…
Large Models in Dialogue for Active Perception and Anomaly Detection
Tzoulio Chamiti, Nikolaos Passalis, Anastasios Tefas
Autonomous aerial monitoring is an important task aimed at gathering information from areas that may not be easily accessible by humans. At the same time, this task often requires…
Using Part-based Representations for Explainable Deep Reinforcement Learning
Manos Kirtas, Konstantinos Tsampazis, Loukia Avramelou +2
Utilizing deep learning models to learn part-based representations holds significant potential for interpretable-by-design approaches, as these models incorporate latent causes obt…
UAV Active Perception and Motion Control for Improving Navigation Using Low-Cost Sensors
Konstantinos Gounis, Nikolaos Passalis, Anastasios Tefas
In this study a model pipeline is proposed that combines computer vision with control-theoretic methods and utilizes low cost sensors. The proposed work enables perception-aware mo…
New Gravitational Wave Discoveries Enabled by Machine Learning
Alexandra E. Koloniari, Evdokia C. Koursoumpa, Paraskevi Nousi +4
The detection of gravitational waves has revolutionized our understanding of the universe, offering unprecedented insights into its dynamics. A major goal of gravitational wave dat…
Deep Active Perception for Object Detection using Navigation Proposals
Stefanos Ginargiros, Nikolaos Passalis, Anastasios Tefas
Deep Learning (DL) has brought significant advances to robotics vision tasks. However, most existing DL methods have a major shortcoming, they rely on a static inference paradigm i…
Photonic Neural Networks and Optics-informed Deep Learning Fundamentals
A. Tsakyridis, M. Moralis-Pegios, G. Giamougiannis +4
The recent explosive compute growth, mainly fueled by the boost of AI and DNNs, is currently instigating the demand for a novel computing paradigm that can overcome the insurmounta…
Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos +10
Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchang…
Non-negative isomorphic neural networks for photonic neuromorphic accelerators
Manos Kirtas, Nikolaos Passalis, Nikolaos Pleros +1
Neuromorphic photonic accelerators are becoming increasingly popular, since they can significantly improve computation speed and energy efficiency, leading to femtojoule per MAC ef…
Multiplicative update rules for accelerating deep learning training and increasing robustness
Manos Kirtas, Nikolaos Passalis, Anastasios Tefas
Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a…
Variational Voxel Pseudo Image Tracking
Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis +2
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and si…
Deep Residual Networks for Gravitational Wave Detection
Paraskevi Nousi, Alexandra E. Koloniari, Nikolaos Passalis +3
Traditionally, gravitational waves are detected with techniques such as matched filtering or unmodeled searches based on wavelets. However, in the case of generic black hole binari…
A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields
Paraskevi Nousi, Emmanouil Mpampis, Nikolaos Passalis +2
In this paper we evaluate the impact of domain shift on human detection models trained on well known object detection datasets when deployed on data outside the distribution of the…
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz +20
We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitationa…
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images
Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis +2
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT). VPIT is the first method that uses voxel pseudo i…
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling
Styliani-Christina Fragkouli, Paraskevi Nousi, Nikolaos Passalis +3
Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries,…
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data usually…
Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
Paraskevi Nousi, Styliani-Christina Fragkouli, Nikolaos Passalis +5
Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive wavefo…
Graph Embedding with Data Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data…
Attention-based Neural Bag-of-Features Learning for Sequence Data
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas +2
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on releva…
AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing
Jorge Peña Queralta, Jenni Raitoharju, Tuan Nguyen Gia +2
Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents t…
Heterogeneous Knowledge Distillation using Information Flow Modeling
Nikolaos Passalis, Maria Tzelepi, Anastasios Tefas
Knowledge Distillation (KD) methods are capable of transferring the knowledge encoded in a large and complex teacher into a smaller and faster student. Early methods were usually l…