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ORCID31st ESANN 2023: Bruges, Belgium
- 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2023, Bruges, Belgium, October 4-6, 2023. 2023
Graph Representation Learning
- Davide Bacciu, Federico Errica, Alessio Micheli
, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon:
Graph Representation Learning. - Domenico Tortorella, Alessio Micheli:
Richness of Node Embeddings in Graph Echo State Networks. - Nicolò Navarin, Luca Pasa, Luca Oneto, Alessandro Sperduti:
An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. - Luis H. M. Torres, Bernardete Ribeiro, Joel Arrais:
Convolutional Transformer via Graph Embeddings for Few-shot Toxicity and Side Effect Prediction. - Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli:
Hidden Markov Models for Temporal Graph Representation Learning. - Francesco Landolfi, Davide Bacciu, Danilo Numeroso:
A Tropical View of Graph Neural Networks. - Weiwei Wang, Stefano Bromuri, Michel Dumontier:
Graph-based Categorical Embedding. - Thanh Vu, Huy Ngo, Bac Le, Thanh Le:
FouriER: Link Prediction by Mixing Tokens with Fourier-enhanced MetaFormer.
Feature selection and dimension reduction
- Fabian Hinder, Barbara Hammer:
Feature Selection for Concept Drift Detection. - Michael Biehl, Sofie Lövdal:
Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA). - Maximilian Münch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann:
Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. - Felipe Contreras, Kerstin Bunte, Reynier Peletier:
Improved the locally aligned ant technique (LAAT) strategy to recover manifolds embedded in strong noise. - Pierre Lambert, John A. Lee, Edouard Couplet, Cyril de Bodt:
Nesterov momentum and gradient normalization to improve t-SNE convergence and neighborhood preservation, without early exaggeration. - Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer:
On Feature Removal for Explainability in Dynamic Environments. - Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer:
Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts. - Benoît Frénay:
A Counterexample to Ockham's Razor and the Curse of Dimensionality: Marginalising Complexity and Dimensionality for GMMs. - Joakim Linja, Joonas Hämäläinen, Tommi Kärkkäinen:
Feature Selection for Multi-label Classification with Minimal Learning Machine. - Daniel Nowak Assis:
Learning with Boosting Decision Stumps for Feature Selection in Evolving Data Streams.
Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness
- Maura Pintor, Ambra Demontis, Battista Biggio:
Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness. - Mohit Kumar, Bernhard Moser, Lukas Fischer:
Secure Federated Learning with Kernel Affine Hull Machines. - Giorgio Piras, Giuseppe Floris, Raffaele Mura, Luca Scionis, Maura Pintor, Battista Biggio, Ambra Demontis:
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization. - David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà, Lea Schönherr:
On the Limitations of Model Stealing with Uncertainty Quantification Models. - Luca Oneto, Sandro Ridella, Davide Anguita:
Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective. - Ana Maria Barragán-Montero, Robin Tilman, Margerie Huet-Dastarac, John A. Lee:
Single-pass uncertainty estimation with layer ensembling for regression: application to proton therapy dose prediction for head and neck cancer.
Fairness and Interpretability, Clustering, and NLP
- Kylliann De Santiago, Marie Szafranski, Christophe Ambroise:
Mixture of stochastic block models for multiview clustering. - Jérémie Bogaert, Emmanuel Jean, Cyril de Bodt, François-Xavier Standaert:
Fine-tuning is not (always) overfitting artifacts. - Paul Moggridge, Na Helian, Yi Sun, Mariana Lilley:
On Instance Weighted Clustering Ensembles. - Zhengxiang Shi, Aldo Lipani:
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis. - Sédrick Stassin, Otmane Amel, Sidi Ahmed Mahmoudi, Xavier Siebert:
Similarity versus Supervision: Best Approaches for HS Code Prediction. - Otmane Amel, Sédrick Stassin, Sidi Ahmed Mahmoudi, Xavier Siebert:
Multimodal Approach for Harmonized System Code Prediction. - Danilo Franco, Luca Oneto, Davide Anguita:
Mitigating Robustness Bias: Theoretical Results and Empirical Evidences. - Denis Martins, Christian Lülf, Fabian Gieseke:
End-to-End Neural Network Training for Hyperbox-Based Classification. - Kodjo Mawuena Amekoe, Mohamed Djallel Dilmi, Hanene Azzag, Zaineb Chelly Dagdia, Mustapha Lebbah, Gregoire Jaffre:
TabSRA: An Attention based Self-Explainable Model for Tabular Learning. - Andrea Ceni, Davide Bacciu, Valerio De Caro, Claudio Gallicchio, Luca Oneto:
Improving Fairness via Intrinsic Plasticity in Echo State Networks. - Martin Bogdan:
Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse. - Karan Bhanot, Dennis Wei, Ioana Baldini, Kristin P. Bennett:
Adversarial Auditing of Machine Learning Models under Compound Shift. - Xi Chen, Giacomo Anerdi, Daniel Stanley Tan, Stefano Bromuri:
Language Modeling in Logistics: Customer Calling Prediction. - Indro Spinelli, Michele Guerra, Filippo Maria Bianchi, Simone Scardapane:
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability.
Quantum Artificial Intelligence
- José D. Martín-Guerrero, Lucas Lamata, Thomas Villmann:
Quantum Artificial Intelligence: A tutorial. - Alessandro Poggiali, Anna Bernasconi, Alessandro Berti, Gianna M. Del Corso, Riccardo Guidotti:
Quantum Feature Selection with Variance Estimation. - Alessandro Berti:
Logarithmic Quantum Forking. - Alexander Engelsberger, Thomas Villmann:
Quantum-ready vector quantization: Prototype learning as a binary optimization problem. - M. Lautaro Hickmann, Arne P. Raulf, Frank Köster, Friedhelm Schwenker, Hans-Martin Rieser:
Potential analysis of a Quantum RL controller in the context of autonomous driving.
Green Machine Learning
- Verónica Bolón-Canedo, Laura Morán-Fernández, Brais Cancela, Amparo Alonso-Betanzos:
Green Machine Learning. - Samuel Suárez-Marcote, Laura Morán-Fernández, Verónica Bolón-Canedo:
Logarithmic division for green feature selection: an information-theoretic approach. - Guillermo Castillo García, Laura Morán-Fernández, Verónica Bolón-Canedo:
Efficient feature selection for domain adaptation using Mutual Information Maximization. - Afonso Lourenço, Carolina Ferraz, Jorge Meira, Goreti Marreiros, Verónica Bolón-Canedo, Amparo Alonso-Betanzos:
Automated green machine learning for condition-based maintenance. - Mariya Shumska, Kerstin Bunte:
Multispectral Texture Classification in Agriculture.
Reinforcement learning and Evolutionary computation
- André Correia, Luís A. Alexandre:
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety. - Phillip Swazinna, Steffen Udluft, Thomas A. Runkler:
Automatic Trade-off Adaptation in Offline RL. - Oliver Kramer:
Enhancing Evolution Strategies with Evolution Path Bias. - Sami Khairy, Prasanna Balaprakash:
Multi-Fidelity Reinforcement Learning with Control Variates. - Guilherme Souza, Priscila Lima, Felipe M. G. França:
Sun Tracking using a Weightless Q-Learning Neural Network. - Brieuc Pinon, Raphaël M. Jungers, Jean-Charles Delvenne:
A model-based approach to meta-Reinforcement Learning: Transformers and tree search. - Gautier Laisné, Nasser Rezzoug, Jean Marc Salotti:
Derivative-Free Optimization Approaches for Force Polytopes Prediction. - Mali Imre Gergely, Gabriela Czibula:
Policy-Based Reinforcement Learning in the Generalized Rock-Paper-Scissors Game.
Classification
- Karol Struniawski, Aleksandra Konopka, Ryszard Kozera:
Performance Evaluation of Activation Functions in Extreme Learning Machine. - Eduardo Mosqueira-Rey, David Vázquez-Lema, Elena Hernández-Pereira:
Evaluating Curriculum Learning Strategies for Pancreatic Cancer Prediction. - Gianluca Coda, Massimo De Gregorio, Antonio Sorgente, Paolo Vanacore:
Improving the DRASiW performance by exploiting its own "Mental Images". - Otávio Oliveira Napoli, Ana Maria de Almeida, José Miguel Sales Dias, Luís Brás Rosário, Edson Borin, Maurício Breternitz Jr.:
Efficient Knowledge Aggregation Methods for Weightless Neural Networks. - Mehrdad Mohannazadeh Bakhtiari, Daniel Staps, Thomas Villmann:
Learning Vector Quantization in Context of Information Bottleneck Theory. - Luis Sanchez, Erzsébet Merényi, Christopher D. Tunnell:
SOM-based Classification and a Novel Stopping Criterion for Astroparticle Applications. - Leopoldo Lusquino Filho, Felipe M. G. França, Priscila Lima:
WiSARD-based Ensemble Learning.
Deep learning and Computer vision
- Matteo Pardi, Domenico Tortorella, Alessio Micheli:
Entropy Based Regularization Improves Performance in the Forward-Forward Algorithm. - Edouard Couplet, Pierre Lambert, Michel Verleysen, John A. Lee, Cyril de Bodt:
On the number of latent representations in deep neural networks for tabular data. - Zhengyang Yu, Jochen Triesch:
CRE: Circle relationship embedding of patches in vision transformer. - Andreas Papachristodoulou, Christos Kyrkou, Stelios Timotheou, Theocharis Theocharides:
Introducing Convolutional Channel-wise Goodness in Forward-Forward Learning. - Hazan Daglayan, Simon Vary, Pierre-Antoine Absil:
An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection. - Mohammed Aldosari, John A. Miller:
On Transformer Autoregressive Decoding for Multivariate Time Series Forecasting. - Nermeen Abou Baker, Uwe Handmann:
Don't waste SAM. - Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl:
Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. - Leonardo Amato, Marta Maschietto, Alessandro Leparulo, Mattia Tambaro, Stefano Vassanelli, Alessandro Sperduti:
Real-time Detection of Evoked Potentials by Deep Learning: a Case Study. - Estelle M. Massart, Vinayak Abrol:
Coordinate descent on the Stiefel manifold for deep neural network training. - Yichun Li, Yuxing Yang, Rajesh Nair, Syed Mohsen Naqvi:
Action-Based ADHD Diagnosis in Video. - Samuele Fonio, Lorenzo Paletto, Mattia Cerrato, Dino Ienco, Roberto Esposito:
Hierarchical priors for Hyperspherical Prototypical Networks. - Helen Schneider, David Biesner, Akash Ashokan, Maximilian Broß, Rebecca Kador, Sandra Halscheidt, Gabor Bagyo, Peter Dankerl, Haissam Ragab, Jin Yamamura, Christoph Labisch, Rafet Sifa:
Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements. - Henda Boudegga, Yaroub Elloumi, Asma Ben Abdallah, Rostom Kachouri, Mouhamed Hédi Bedoui:
Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture. - Quoc-Huy Trinh, Hieu Nguyen, Van Nguyen, Xuan-Mao Nguyen, Hai-Dang Nguyen:
Graph for Transformer Feature: A New Approach for Face Anti-Spoofing. - Alexandra-Ioana Albu:
Temporal Ensembling-based Deep k-Nearest Neighbours for Learning with Noisy Labels. - Leandro H. de S. Silva, Agostinho A. F. Júnior, Bruno J. T. Fernandes, George O. de A. Azevedo, Sergio C. Oliveira:
Evaluation of Contrastive Learning for Electronic Component Detection.
Sequential data, and Meta-learning
- Tanguy Bosser, Souhaib Ben Taieb:
Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes. - Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu:
A Protocol for Continual Explanation of SHAP. - Claudio Gallicchio, Andrea Ceni:
Residual Reservoir Computing Neural Networks for Time-series Classification. - Tameem Adel:
Probabilistic Adaptation for Meta-Learning. - Fatoumata Dama, Christine Sinoquet, Corinne Lejus-Bourdeau:
A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. - Giulia Marchello, Marco Corneli, Charles Bouveyron:
Deep dynamic co-clustering of streams of count data: a new online Zip-dLBM. - Valerio De Caro, Antonio Di Mauro, Davide Bacciu, Claudio Gallicchio:
Communication-Efficient Ridge Regression in Federated Echo State Networks. - Antoine Hubermont, Elio Tuci, Nicola De Quattro:
Simultaneous failures classification in a predictive maintenance case. - Alberto Meola, Sören Weinrich:
Hybrid modelling of dynamic anaerobic digestion process in full-scale with LSTM NN and BMP measurements. - Oliver Kramer, Jill Baumann:
Wind Power Prediction with ETSformer. - Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash:
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Machine Learning Applied to Sign Language
- Jérôme Fink, Mathieu De Coster, Joni Dambre, Benoît Frénay:
Trends and Challenges for Sign Language Recognition with Machine Learning. - Fabrizio Nunnari, Annette Rios, Uwe Reichel, Chirag Bhuvaneshwara, Panagiotis Paraskevas Filntisis, Petros Maragos, Felix Burkhardt, Florian Eyben, Björn W. Schuller, Sarah Ebling:
Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions. - Lee Kezar, Tejas Srinivasan, Riley Carlin, Jesse Thomason, Zed Sevcikova Sehyr, Naomi Caselli:
Exploring Strategies for Modeling Sign Language Phonology. - Javier Martinez Rodriguez, Martha A. Larson, Louis ten Bosch:
Exploring the Importance of Sign Language Phonology for a Deep Neural Network. - Alvaro Leandro Cavalcante Carneiro, Denis Henrique Pinheiro Salvadeo, Lucas de Brito Silva:
Large-scale dataset and benchmarking for hand and face detection focused on sign language. - Dinh Nam Pham, Vera Czehmann, Eleftherios Avramidis:
Disambiguating Signs: Deep Learning-based Gloss-level Classification for German Sign Language by Utilizing Mouth Actions.
Efficient Learning in Spiking Neural Networks
- Alex Rast, Mario Antoine Aoun, Eleni Elia, Nigel T. Crook:
Efficient Learning in Spiking Models. - Naresh Balaji Ravichandran, Anders Lansner, Pawel Andrzej Herman:
Spiking neural networks with Hebbian plasticity for unsupervised representation learning. - Nigel T. Crook, Alex Rast, Eleni Elia, Mario Antoine Aoun:
Functional Resonant Synaptic Clusters for Decoding Time-Structured Spike Trains. - Nicola Russo, Yuzhong Wan, Thomas Madsen, Konstantin Nikolic:
Pattern Recognition Spiking Neural Network for Classification of Chinese Characters. - Louis Le Coeur, Nick Riedman, Saarthak Sarup, Kwabena Boahen:
Energy-efficient detection of a spike sequence.
Anomaly Detection, and Learning Algorithms
- Sukanya Patra, Le Thi Khanh Hien, Souhaib Ben Taieb:
Anomaly detection in irregular image sequences for concentrated solar power plants. - Adrian Alan Pol, Ekaterina Govorkova, Sonja Grönroos, Nadezda Chernyavskaya, Philip C. Harris, Maurizio Pierini, Isobel Ojalvo, Peter Elmer:
Knowledge Distillation for Anomaly Detection. - Fjola Hyseni, Nicolas P. Rougier, Arthur Leblois:
Comparative study of the synfire chain and ring attractor model for timing in the premotor nucleus in male Zebra Finches. - Anne-Sophie Collin, Cyril de Bodt, Dounia Mulders, Christophe De Vleeschouwer:
Don't skip the skips: autoencoder skip connections improve latent representation discrepancy for anomaly detection. - Thomas Villmann, Ronny Schubert, Marika Kaden:
Variants of Neural Gas for Regression Learning. - Jungeun Yoon, Dasong Yu, Youngjae Lee:
Hybrid Deep Learning-Based Air and Water Quality Prediction Model. - Sophie Adama, Martin Bogdan:
Sleep analysis in a CLIS patient using soft-clustering: a case study. - Armielle Noulapeu Ngaffo, Julien Albert, Benoît Frénay, Gilles Perrouin:
FairBayRank: A Fair Personalized Bayesian Ranker. - Felix Weiske, Jens Jäkel:
Robust and Cheap Safety Measure for Exoskeletal Learning Control with Estimated Uniform PAC (EUPAC).
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