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ORCID30th ESANN 2022: Bruges, Belgium
- 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges, Belgium, October 5-7, 2022. 2022
- Andrea Valenti, Davide Bacciu:
Modular Representations for Weak Disentanglement. - Verónica Bolón-Canedo, Guillermo Castillo García, Laura Morán-Fernández:
Feature selection for transfer learning using particle swarm optimization and complexity measures. - Sorina Mustatea
, Michaël Aupetit, Jaakko Peltonen, Sylvain Lespinats, Denys Dutykh:
Supervised dimensionality reduction technique accounting for soft classes. - Hervé Frezza-Buet:
Graph-Induced Geodesics Approximation for Non-Euclidian K-Means. - Luis A. Q. Villon, Zachary Susskind, Alan T. L. Bacellar, Igor D. S. Miranda, Leandro Santiago de Araújo, Priscila M. V. Lima, Maurício Breternitz Jr., Lizy K. John, Felipe M. G. França, Diego Leonel Cadette Dutra:
A WiSARD-based conditional branch predictor. - Alan T. L. Bacellar, Zachary Susskind, Luis A. Q. Villon, Igor D. S. Miranda, Leandro Santiago de Araújo, Diego Leonel Cadette Dutra, Maurício Breternitz Jr., Lizy K. John, Priscila M. V. Lima, Felipe M. G. França:
Distributive Thermometer: A New Unary Encoding for Weightless Neural Networks. - Zachary Susskind, Alan T. L. Bacellar, Aman Arora, Luis A. Q. Villon, Renan Mendanha, Leandro Santiago de Araújo, Diego Leonel Cadette Dutra, Priscila M. V. Lima, Felipe M. G. França, Igor D. S. Miranda, Maurício Breternitz Jr., Lizy K. John:
Pruning Weightless Neural Networks. - Massimo De Gregorio, Alfonso Di Costanzo, Andrea Motta, Debora Paris, Antonio Sorgente:
Classification of preclinical markers in Alzheimer's disease via WiSARD classifier. - Anthony Fillion, Thibaut Kulak, François Blayo:
A bayesian variational principle for dynamic self organizing maps. - Roger Bagué-Masanés, Verónica Bolón-Canedo, Beatriz Remeseiro:
The role of feature selection in personalized recommender systems. - Gerrit Luimstra, Kerstin Bunte:
Adaptive Gabor Filters for Interpretable Color Texture Classification.
Continual Learning beyond classification
- Alexander Gepperth, Timothée Lesort:
Tutorial - Continual Learning beyond classification. - Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu:
Continual Learning for Human State Monitoring. - Michele Resta, Davide Bacciu:
Continual Incremental Language Learning for Neural Machine Translation. - Andrii Krutsylo, Pawel Morawiecki:
Diverse Memory for Experience Replay in Continual Learning.
Classification
- André Artelt, Roel Visser, Barbara Hammer:
Model Agnostic Local Explanations of Reject. - Maximilian Münch, Christoph Raab, Simon Heilig, Manuel Röder, Frank-Michael Schleif:
Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. - Berardino Barile, Pooya Ashtari, Françoise Durand-Dubief, Frederik Maes, Dominique Sappey-Marinier, Sabine Van Huffel:
A Kernel Based Multilinear SVD Approach for Multiple Sclerosis Profiles Classification. - João Gabriel Corrêa Krüger, Jean Paul Barddal, Alceu de Souza Britto Jr.:
A Machine Learning Approach for School Dropout Prediction in Brazil. - Nadzeya Dzemidovich, Alexander Gepperth:
An empirical comparison of generators in replay-based continual learning. - Steven Michiels, Cédric De Schryver, Lynn Houthuys, Frederik Vogeler, Frederik Desplentere:
Machine learning for automated quality control in injection moulding manufacturing. - Luca Oneto, Simone Minisi, Andrea Garrone, Renzo Canepa, Carlo Dambra, Davide Anguita:
Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets. - Mohammadmahdi Ghahramani, Fabio Aiolli:
Price direction prediction in financial markets, using Random Forest and Adaboost.
Learning theory and principles
- Seyedsaman Emami, Gonzalo Martínez-Muñoz:
Multioutput Regression Neural Network Training via Gradient Boosting. - Luca Oneto, Sandro Ridella, Davide Anguita:
Do We Really Need a New Theory to Understand the Double-Descent? - Adrien Pavão, Isabelle Guyon, Zhengying Liu:
Filtering participants improves generalization in competitions and benchmarks. - Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. - Oliver Kramer:
A Fast and Simple Evolution Strategy with Covariance Matrix Estimation. - Quinten Van Baelen, Peter Karsmakers:
Constraint Guided Gradient Descent: Guided Training with Inequality Constraints. - Mirko Polato, Fabio Aiolli, Luca Bergamin, Tommaso Carraro:
Bayes Point Rule Set Learning. - Jirí Tumpach, Jan Koza, Martin Holena:
Neural-network-based estimation of normal distributions in black-box optimization.
Deep learning, signal, image
- Suresh Kirthi Kumaraswamy, Alexey Ozerov, Ngoc Q. K. Duong, Anne Lambert, François Schnitzler, Patrick Fontaine:
Feature Compression Using Dynamic Switches in Multi-split CNNs. - Felix Meyer-Veit, Rania Rayyes, Andreas O. H. Gerstner, Jochen J. Steil:
Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection. - Giovanni Bonetta, Rossella Cancelliere:
Lightening CNN architectures by regularization driven weights' pruning. - Gaëlle Milon-Harnois, Nisrine Jrad, Daniel Schang, Patrick Van Bogaert, Pierre Chauvet:
1D vs 2D convolutional neural networks for scalp high frequency oscillations identification. - Dingge Liang, Marco Corneli, Charles Bouveyron, Pierre Latouche:
Deep latent position model for node clustering in graphs. - Matthias Kissel, Klaus Diepold:
Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices. - Joseph Rynkiewicz:
Deep networks with ReLU activation functions can be smooth statistical models. - István Megyeri, Ammar Al-Najjar:
PCA improves the adversarial robustness of neural networks. - Nermeen Abou Baker, David Rohrschneider, Uwe Handmann:
Battery detection of XRay images using transfer learning. - Lukas Enderich, Simon Heming:
Real-time capable Ensemble Estimation for 2D Object Detection. - Nikhil Kilari, Gaurab Bhattacharya, K. Pavan Kumar Reddy, Jayavardhana Gubbi, Arpan Pal:
Appearance-Context aware Axial Attention for Fashion Landmark Detection. - Rémi Delogne, Vincent Schellekens, Laurent Jacques:
ROP inception: signal estimation with quadratic random sketching. - Mohammed El Amine Mokhtari, Virginie Vandenbulcke, Sohaib Laraba, Matei Mancas, Elias Ennadifi, Mohamed Lamine Tazir, Bernard Gosselin:
Semi-synthetic Data for Automatic Drone Shadow Detection. - Tomasz Gutowski:
Deep learning for Parkinson's disease symptom detection and severity evaluation using accelerometer signal.
Anomaly and change point detection
- Madalina Olteanu, Fabrice Rossi, Florian Yger:
Challenges in anomaly and change point detection. - Jean Coussirou, Thomas Vanaret, Jérôme Lacaille:
Anomaly detections on the oil system of a turbofan engine by a neural autoencoder. - Fabian Hinder, André Artelt, Valerie Vaquet, Barbara Hammer:
Contrasting Explanation of Concept Drift. - Yacine Bel-Hadj, Wout Weijtjens, Francisco de Nolasco Santos:
Anomaly detection and representation learning in an instrumented railway bridge.
Deep Semantic Segmentation Models in Computer Vision
- Paolo Andreini, Giovanna Maria Dimitri:
Deep Semantic Segmentation Models in Computer Vision. - Daniela Cuza, Andrea Loreggia, Alessandra Lumini, Loris Nanni:
Deep Semantic Segmentation in Skin Detection. - Simone Bonechi:
A weakly supervised approach to skin lesion segmentation. - Paolo Andreini, Niccolò Pancino, Filippo Costanti, Gabriele Eusepi, Barbara Toniella Corradini:
A Deep Learning approach for oocytes segmentation and analysis. - Duccio Meconcelli, Simone Bonechi, Giovanna Maria Dimitri:
Deep Learning Approaches for mice glomeruli segmentation. - Lydia Abady, Giovanna Maria Dimitri, Mauro Barni:
Detection and Localization of GAN Manipulated Multi-spectral Satellite Images.
Regression and forecasting
- Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, José Henrique Kleinübing Larcher, Cristiane Canton, Viviana Cocco Mariani, Leandro dos Santos Coelho:
Wind power forecasting based on bagging extreme learning machine ensemble model. - Michael Potter, Ilkay Yildiz Potter, Octavia I. Camps, Mario Sznaier:
Dynamics-aware Representation Learning via Multivariate Time Series Transformers. - Francisco de Nolasco Santos, Pietro D'Antuono, Nymfa Noppe, Wout Weijtjens, Christof Devriendt:
Minkowski logarithmic error: A physics-informed neural network approach for wind turbine lifetime assessment. - Neta Rabin, Ben Hen, Ángela Fernández:
Improving Laplacian Pyramids Regression with Localization in Frequency and Time. - Benoît Loucheur, Pierre-Antoine Absil, Michel Journée:
Gap filling in air temperature series by matrix completion methods. - Francisco Pereira, Helio Silva, João Gomes, Javam C. Machado:
Predicting Test Execution Times with Asymmetric Random Forests.
Recurrent learning and reservoir computing
- Andrea Ceni, Claudio Gallicchio:
Orthogonality in Additive Echo State Networks. - Pierre Poitier, Jérôme Fink, Benoît Frénay:
Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization. - Valerio De Caro, Claudio Gallicchio, Davide Bacciu:
Federated Adaptation of Reservoirs via Intrinsic Plasticity. - Arun Pandey, Hannes De Meulemeester, Henri De Plaen, Bart De Moor, Johan A. K. Suykens:
Recurrent Restricted Kernel Machines for Time-series Forecasting. - Luca Argentieri, Claudio Gallicchio, Alessio Micheli:
Input Routed Echo State Networks.
Natural language processing, and recommender systems
- Zhengxiang Shi, Pin Ni, Meihui Wang, To Eun Kim, Aldo Lipani:
Attention-based Ingredient Phrase Parser. - Philip Kenneweg, Sarah Schröder, Barbara Hammer:
Neural Architecture Search for Sentence Classification with BERT. - David Young, Douglas J. Leith:
High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits.
Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine
- Thomas Villmann, Jonas S. Almeida, John A. Lee, Susana Vinga:
Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine. - Ignacio Díaz Blanco, José M. Enguita, Diego García-Pérez, Ana González-Muñiz, Abel Alberto Cuadrado Vega, Maria Dolores Chiara-Romero, Nuria Valdés:
Interactive dual projections for gene expression analysis. - Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann:
Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. - Ignacio Díaz Blanco, José M. Enguita, Diego García-Pérez, Maria Dolores Chiara-Romero, Nuria Valdés, Ana González-Muñiz, Abel Alberto Cuadrado Vega:
Interactive visual analytics for medical data: application to COVID-19 clinical information during the first wave. - Helen Schneider, David Biesner, Sebastian Nowak, Yannik C. Layer, Maike Theis, Wolfgang Block, Benjamin Wulff, Alois M. Sprinkart, Ulrike I. Attenberger, Rafet Sifa:
Improving Intensive Care Chest X-Ray Classification by Transfer Learning and Automatic Label Generation.
Concept drift
- Johannes Brinkrolf, Valerie Vaquet, Fabian Hinder, Patrick Menz, Udo Seiffert, Barbara Hammer:
Federated learning vector quantization for dealing with drift between nodes. - Patrick Menz, Valerie Vaquet, Barbara Hammer, Udo Seiffert:
From hyperspectral to multispectral sensing - from simulation to reality: A comprehensive approach for calibration model transfer. - Joanna Komorniczak, Pawel Ksieniewicz:
Data stream generation through real concept's interpolation.
Deep Learning for Graphs
- Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. - Domenico Tortorella, Alessio Micheli:
Beyond Homophily with Graph Echo State Networks. - Federico Caldart, Luca Pasa, Luca Oneto, Alessandro Sperduti, Nicolò Navarin:
Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. - Raphaël Romero, Tijl De Bie:
Embedding-based next song recommendation for playlists. - Gaia Saveri:
Graph Neural Networks for Propositional Model Counting. - Francesco Landolfi:
Revisiting Edge Pooling in Graph Neural Networks.
Reinforcement learning
- Oren Neumann, Claudius Gros:
Size Scaling in Self-Play Reinforcement Learning. - Andreas Mazur, André Artelt, Barbara Hammer:
Improving Zorro Explanations for Sparse Observations with Dense Proxy Data. - Oytun Yapar, Erhan Öztop:
Reinforcement learning for constructing low density sign representations of Boolean functions. - Jianyong Xue, Frédéric Alexandre:
Developmental Modular Reinforcement Learning. - Yi Zhao, Rinu Boney, Alexander Ilin, Juho Kannala, Joni Pajarinen:
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning.
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last updated on 2025-01-28 22:38 CET by the dblp team
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