249 citations · 1.2k across the 77 of their papers we have counts for
18 papers · 1 filter
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger +6
Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics…
On Quantitative Evaluations of Counterfactuals
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metri…
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series
Błażej Leporowski, Casper Hansen, Alexandros Iosifidis
Industrial processes are monitored by a large number of various sensors that produce time-series data. Deep Learning offers a possibility to create anomaly detection methods that c…
Remote Multilinear Compressive Learning with Adaptive Compression
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection…
Bilinear Input Normalization for Neural Networks in Financial Forecasting
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is…
Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting
Mostafa Shabani, Alexandros Iosifidis
Financial market analysis, especially the prediction of movements of stock prices, is a challenging problem. The nature of financial time-series data, being non-stationary and nonl…
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving
Błażej Leporowski, Daniella Tola, Casper Hansen +1
Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for…
Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation
Negar Heidari, Alexandros Iosifidis
Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to over…
Multi-Exit Vision Transformer for Dynamic Inference
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
Deep neural networks can be converted to multi-exit architectures by inserting early exit branches after some of their intermediate layers. This allows their inference process to b…
Learning distinct features helps, provably
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average -distance between the…
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e.g., deep neural networks. We pr…
Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj +1
In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes…
Knowledge Distillation By Sparse Representation Matching
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Knowledge Distillation refers to a class of methods that transfers the knowledge from a teacher network to a student network. In this paper, we propose Sparse Representation Matchi…
Visualising Deep Network's Time-Series Representations
Błażej Leporowski, Alexandros Iosifidis
Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model. There exist a…
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
Mert Seker, Anssi Männistö, Alexandros Iosifidis +1
The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of th…
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification. Recently, automated augment…
Ensembling object detectors for image and video data analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We fur…
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset
Błażej Leporowski, Daniella Tola, Casper Hansen +1
Screwdriving is one of the most popular industrial processes. As such, it is increasingly common to automate that procedure by using various robots. Even though the automation incr…