59 citations · 282 across the 64 of their papers we have counts for
69 papers
Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics
Anubha Goel, Illia Oleksiienko, Mateusz Wilinski +2
Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node activation times. Application…
Explicit Dropout: Deterministic Regularization for Transformer Architectures
Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis
Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives.…
xHAP: Cross-Modal Attention for Haptic Feedback Estimation in the Tactile Internet
Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang
The Tactile Internet requires ultra-low latency and high-fidelity haptic feedback to enable immersive teleoperation. A key challenge is to ensure ultra-reliable and low-latency tra…
DeepCoT: Deep Continual Transformers for Real-Time Inference on Data Streams
Ginés Carreto Picón, Peng Yuan Zhou, Qi Zhang +1
Transformer-based models have dramatically increased their size and parameter count to tackle increasingly complex tasks. At the same time, there is a growing demand for high perfo…
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis +1
Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequen…
InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation
Sanyam Jain, Khuram Naveed, Illia Oleksiienko +2
This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D poi…
Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain +3
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized…
PRISM: Distributed Inference for Foundation Models at Edge
Muhammad Azlan Qazi, Alexandros Iosifidis, Qi Zhang
Foundation models (FMs) have achieved remarkable success across a wide range of applications, from image classification to natural langurage processing, but pose significant challe…
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
Sanyam Jain, Bruna Neves de Freitas, Andreas Basse-OConnor +2
Interest in generating realistic synthetic medical images has grown in recent years. Such datasets can mitigate the scarcity of public data for artificial intelligence research and…
Variational Graph Convolutional Neural Networks
Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis
Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in…
Delay Bound Relaxation with Deep Learning-based Haptic Estimation for Tactile Internet
Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang
Haptic teleoperation typically demands sub-millisecond latency and ultra-high reliability (99.999%) in Tactile Internet. At a 1 kHz haptic signal sampling rate, this translates int…
Classifying and Clustering Trading Agents
Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis +1
The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but…
Learning hidden cascades via classification
Derrick Gilchrist Edward Manoharan, Anubha Goel, Alexandros Iosifidis +2
The spreading dynamics in social networks are often studied under the assumption that individuals' statuses, whether informed or infected, are fully observable. However, in many re…
Deep Reinforcement Learning-based Video-Haptic Radio Resource Slicing in Tactile Internet
Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang
Enabling video-haptic radio resource slicing in the Tactile Internet requires a sophisticated strategy to meet the distinct requirements of video and haptic data, ensure their sync…
Continual Low-Rank Scaled Dot-product Attention
Ginés Carreto Picón, Illia Oleksiienko, Lukas Hedegaard +2
Transformers are widely used for their ability to capture data relations in sequence processing, with great success for a wide range of static tasks. However, the computational and…
Are LLM-based methods good enough for detecting unfair terms of service?
Mirgita Frasheri, Arian Bakhtiarnia, Lukas Esterle +1
Countless terms of service (ToS) are being signed everyday by users all over the world while interacting with all kinds of apps and websites. More often than not, these online cont…
Geometric Deep Learning for Computer-Aided Design: A Survey
Negar Heidari, Alexandros Iosifidis
Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and enginee…
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and compu…
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
Mehdi Rafiei, Alexandros Iosifidis
In anomaly detection, identification of anomalies across diverse product categories is a complex task. This paper introduces a new model by including class discriminative propertie…
On Pixel-level Performance Assessment in Anomaly Detection
Mehdi Rafiei, Toby P. Breckon, Alexandros Iosifidis
Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a comple…
Variational Inference for GARCH-family Models
Martin Magris, Alexandros Iosifidis
The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust appr…
Cryptocurrency Portfolio Optimization by Neural Networks
Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen +2
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on…
Curiosity-Driven Reinforcement Learning based Low-Level Flight Control
Amir Ramezani Dooraki, Alexandros Iosifidis
Curiosity is one of the main motives in many of the natural creatures with measurable levels of intelligence for exploration and, as a result, more efficient learning. It makes it…
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos
Błażej Leporowski, Arian Bakhtiarnia, Nicole Bonnici +4
We introduce the first audio-visual dataset for traffic anomaly detection taken from real-world scenes, called MAVAD, with a diverse range of weather and illumination conditions. I…