59 citations · 282 across the 64 of their papers we have counts for
31 papers · 1 filter
Physics-guided machine learning for wind-farm power prediction: Toward interpretability and generalizability
Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar
With the increasing amount of available data from simulations and experiments, research for the development of data-driven models for wind-farm power prediction has increased signi…
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
Andrea Cavagna, Nan Li, Alexandros Iosifidis +1
This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
Zhongtian Dong, Nan Li, Alexandros Iosifidis +1
For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Mostafa Shabani, Martin Magris, George Tzagkarakis +2
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchroniz…
Attention-based Feature Compression for CNN Inference Offloading in Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel a…
Bayesian Learning for Neural Networks: an algorithmic survey
Martin Magris, Alexandros Iosifidis
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topic and the multitude of ingredients involved therein, besides the complexity of t…
Structured Pruning Adapters
Lukas Hedegaard, Aman Alok, Juby Jose +1
Adapters are a parameter-efficient alternative to fine-tuning, which augment a frozen base network to learn new tasks. Yet, the inference of the adapted model is often slower than…
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
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 Edge…
Manifold Gaussian Variational Bayes on the Precision Matrix
Martin Magris, Mostafa Shabani, Alexandros Iosifidis
We propose an optimization algorithm for Variational Inference (VI) in complex models. Our approach relies on natural gradient updates where the variational space is a Riemann mani…
Layer Ensembles
Illia Oleksiienko, Alexandros Iosifidis
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and c…
HyperTime: Implicit Neural Representation for Time Series
Elizabeth Fons, Alejandro Sztrajman, Yousef El-laham +2
Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general a…
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images proces…
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification
Mostafa Shabani, Dat Thanh Tran, Juho Kanniainen +1
Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a mode…
Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing. To ensure inference accuracy in inference task par…
Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. To avoid inference accuracy loss in infere…
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Arian Bakhtiarnia, Błażej Leporowski, Lukas Esterle +1
Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various ta…
Efficient High-Resolution Deep Learning: A Survey
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often nee…
Variational Neural Networks
Illia Oleksiienko, Dat Thanh Tran, Alexandros Iosifidis
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each inp…
Dynamic Split Computing for Efficient Deep Edge Intelligence
Arian Bakhtiarnia, Nemanja Milošević, Qi Zhang +2
Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task due to their limited computational resources. Thus, demanding tasks are often entirely offload…
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning
Martin Magris, Mostafa Shabani, Alexandros Iosifidis
We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for effi…
Data-driven fluid mechanics of wind farms: A review
Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar
With the growing number of wind farms over the last decades and the availability of large datasets, research in wind-farm flow modeling - one of the key components in optimizing th…
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
Lukas Hedegaard, Alexandros Iosifidis
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inf…
Automatic Image Content Extraction: Operationalizing Machine Learning in Humanistic Photographic Studies of Large Visual Archives
Anssi Männistö, Mert Seker, Alexandros Iosifidis +1
Applying machine learning tools to digitized image archives has a potential to revolutionize quantitative research of visual studies in humanities and social sciences. The ability…
Continual Spatio-Temporal Graph Convolutional Networks
Lukas Hedegaard, Negar Heidari, Alexandros Iosifidis
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantl…