249 citations · 1.2k across the 77 of their papers we have counts for
25 papers · 1 filter
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer
Mohammad Soltanian, Junaid Malik, Jenni Raitoharju +3
Automatic classification of speech commands has revolutionized human computer interactions in robotic applications. However, employed recognition models usually follow the methodol…
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the…
On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph. Most o…
Augmenting transferred representations for stock classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Stock classification is a challenging task due to high levels of noise and volatility of stocks returns. In this paper we show that using transfer learning can help with this task,…
Evaluating data augmentation for financial time series classification
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng +2
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their us…
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal…
Performance Indicator in Multilinear Compressive Learning
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e.…
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…
Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear…
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +2
Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore…
CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds
A. H. Nielsen, A. Iosifidis, H. Karstoft
Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect clouds forecasts can lead to major uncertainty in the over…
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…
Probabilistic Color Constancy
Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis +3
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene…
Self-Organized Operational Neural Networks with Generative Neurons
Serkan Kiranyaz, Junaid Malik, Habib Ben Abdallah +3
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional Neural Networks (CNNs) such as netw…
Saliency-based Weighted Multi-label Linear Discriminant Analysis
Lei Xu, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a new variant of Linear Discriminant Analysis (LDA) to solve multi-label classification tasks. The proposed method is based on a probabilistic model for d…
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
Lukas Hedegaard, Omar Ali Sheikh-Omar, Alexandros Iosifidis
Domain Adaptation is the process of alleviating distribution gaps between data from different domains. In this paper, we show that Domain Adaptation methods using pair-wise relatio…
Not all domains are equally complex: Adaptive Multi-Domain Learning
Ali Senhaji, Jenni Raitoharju, Moncef Gabbouj +1
Deep learning approaches are highly specialized and require training separate models for different tasks. Multi-domain learning looks at ways to learn a multitude of different task…
Ellipsoidal Subspace Support Vector Data Description
Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data…
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. Existing methods use a network structure defined by t…
Supervised Domain Adaptation using Graph Embedding
Lukas Hedegaard Morsing, Omar Ali Sheikh-Omar, Alexandros Iosifidis
Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transf…
Data Normalization for Bilinear Structures in High-Frequency Financial Time-series
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj +1
Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since the financial marke…
Multilinear Compressive Learning with Prior Knowledge
Dat Thanh Tran, Moncef Gabbouj, Alexandros Iosifidis
The recently proposed Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system that takes into accou…
Incremental Fast Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj +1
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kern…
Automatic image-based identification and biomass estimation of invertebrates
Johanna Ärje, Claus Melvad, Mads Rosenhøj Jeppesen +8
Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessi…