63 citations · 241 across the 21 of their papers we have counts for
23 papers
Voting Profiles Admitting All Candidates as Knockout Winners
Bernard De Baets, Emilio De Santis
A set of candidates is presented to a commission. At every round, each member of this commission votes by pairwise comparison, and one-half of the candidates is deleted from…
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
Leonardo Scabini, Andre Sacilotti, Kallil M. Zielinski +3
Texture, a significant visual attribute in images, has been extensively investigated across various image recognition applications. Convolutional Neural Networks (CNNs), which have…
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas +5
In recent years, we have seen many advancements in wood species identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy, and Direct Analysis in Real Time (DART)…
Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data
Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf +4
Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzi…
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning
Sina Borzooei, Leonardo Scabini, Gisele Miranda +7
Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community comp…
The Hyperdimensional Transform for Distributional Modelling, Regression and Classification
Pieter Dewulf, Bernard De Baets, Michiel Stock
Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications. Although the main ideas already took form…
The Hyperdimensional Transform: a Holographic Representation of Functions
Pieter Dewulf, Michiel Stock, Bernard De Baets
Integral transforms are invaluable mathematical tools to map functions into spaces where they are easier to characterize. We introduce the hyperdimensional transform as a new kind…
Conditional validity of heteroskedastic conformal regression
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Conformal prediction, and split conformal prediction as a specific implementation, offer a distribution-free approach to estimating prediction intervals with statistical guarantees…
Triangular norms on bounded trellises
Lemnaouar Zedam, Bernard De Baets
In this paper, we introduce the notion of a t-norm on bounded pseudo-ordered sets and in particular on bounded trellises (also known as weakly associative lattices), and provide so…
Improving Deep Neural Network Random Initialization Through Neuronal Rewiring
Leonardo Scabini, Bernard De Baets, Odemir M. Bruno
The deep learning literature is continuously updated with new architectures and training techniques. However, weight initialization is overlooked by most recent research, despite s…
Valid prediction intervals for regression problems
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble methods, direct inter…
The Impact of Hurricanes on the Oceanographic Conditions in the Exclusive Economic Zone of Cuba
Dailé Avila-Alonso, Jan M. Baetens, Rolando Cardenas +1
In this work, we analysed the satellite-based responses of sea surface temperature (SST) and chlorophyll-a (chl-a) concentration in the waters of the Exclusive Economic Zone (EEZ)…
Number-conserving cellular automata with a von Neumann neighborhood of range one
Barbara Wolnik, Adam Dzedzej, Jan M. Baetens +1
We present necessary and sufficient conditions for a cellular automaton with a von Neumann neighborhood of range one to be number-conserving. The conditions are formulated for any…
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Michiel Stock, Krzysztof Dembczynski, Bernard De Baets +1
Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predict…
An evolutionary approach to the identification of Cellular Automata based on partial observations
Witold Bołt, Jan M. Baetens, Bernard De Baets
In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown…
Participatory Patterns in an International Air Quality Monitoring Initiative
Alina Sîrbu, Martin Becker, Saverio Caminiti +17
The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment…
On the decomposition of stochastic cellular automata
Witold Bołt, Jan M. Baetens, Bernard DeBaets
In this paper we present two interesting properties of stochastic cellular automata that can be helpful in analyzing the dynamical behavior of such automata. The first property all…
Improved texture image classification through the use of a corrosion-inspired cellular automaton
Núbia Rosa da Silva, Pieter Van der Weeën, Bernard De Baets +1
In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from co…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Tapio Pahikkala, Antti Airola, Michiel Stock +2
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditione…
Slime mould imitation of Belgian transport networks: redundancy, bio-essential motorways, and dissolution
Andrew Adamatzky, Bernard De Baets, Wesley Van Dessel
Belgium is amongst few artificial countries, established on purpose, when Dutch and French speaking parts were joined in a single unit. This makes Belgium a particularly interestin…
A kernel-based framework for learning graded relations from data
Willem Waegeman, Tapio Pahikkala, Antti Airola +3
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations betwe…