249 citations · 1.2k across the 77 of their papers we have counts for
94 papers
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
Eljas Linna, Kestutis Baltakys, Derrick Manoharan +2
Deep Limit Order Book forecasting models capture nonlinear market dynamics, but their ability to quantify the effects of counterfactual order book messages has not been systematica…
Convolutional autoencoder-based multimodal one-class classification
Firas Laakom, Fahad Sohrab, Jenni Raitoharju +2
One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable fo…
On Feature Diversity in Energy-based Models
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-m…
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
Mehdi Rafiei, Alexandros Iosifidis
Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems. However, exist…
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…
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from t…
Recognition of Defective Mineral Wool Using Pruned ResNet Models
Mehdi Rafiei, Dat Thanh Tran, Alexandros Iosifidis
Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recog…
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
Negar Heidari, Alexandros Iosifidis
Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability and accordingly its performance in different learning tasks.…
Efficient CNN with uncorrelated Bag of Features pooling
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +1
Despite the superior performance of CNN, deploying them on low computational power devices is still limited as they are typically computationally expensive. One key cause of the hi…
A multimodal stress detection dataset with facial expressions and physiological signals
Majid Hosseini, Fahad Sohrab, Raju Gottumukkala +5
Affective computing has garnered the attention and interest of researchers in recent years, as there is a need for AI systems to better understand and react to human emotions. Howe…
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…
Generalized Reference Kernel for One-class Classification
Jenni Raitoharju, Alexandros Iosifidis
In this paper, we formulate a new generalized reference kernel hoping to improve the original base kernel using a set of reference vectors. Depending on the selected reference vect…
How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective
Dat Thanh Tran, Juho Kanniainen, Alexandros Iosifidis
Research on limit order book markets has been rapidly growing and nowadays high-frequency full order book data is widely available for researchers and practitioners. However, it is…
Self-attention fusion for audiovisual emotion recognition with incomplete data
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data…
Self-Attention Neural Bag-of-Features
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this work, we propose several attention formulations for multivariate sequence data. We build on top of the recently introduced 2D-Attention and reformulate the attention learni…
Fighting Money Laundering with Statistics and Machine Learning
Rasmus Jensen, Alexandros Iosifidis
Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this pap…
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…