<TitleType>01</TitleType> <TitleText textcase="01">ESANN 2023 - Proceedings</TitleText> <Subtitle textcase="01">31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</Subtitle>

06 3012405004818 Ciaco scrl onixsuitesupport@onixsuite.com 20261010 eng

COM.ONIXSUITE.9782875870872 03 02 i6doc 01 SKU 105920 02 2875870874 03 9782875870872 15 9782875870872 BC 1 <TitleType>01</TitleType> <TitleText textcase="01">ESANN 2023 - Proceedings</TitleText> <Subtitle textcase="01">31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning</Subtitle> 01 GCOI 28001100881180 1 A01 Collectif 01 eng 07 712 03 TEC000000 U 29 2012 3193 INFORMATIQUE 24 INTERNET Informatique 01 05 01 06 01 Each year, around 120-150 specialists attend ESANN, in order to present their latest results and comprehensive surveys, and to discuss the future developments in this field. The ESANN 2023 conference follows this tradition, while continuously adapting its scope to the new developments in the field. 03 Each year, around 120-150 specialists attend ESANN, in order to present their latest results and comprehensive surveys, and to discuss the future developments in this field. The ESANN 2023 conference follows this tradition, while continuously adapting its scope to the new developments in the field. 02 Since 1993, ESANN has become a reference for researchers on fundamental and theoretical aspects of artificial neural networks, computational intelligence, machine learning and related topics. 04 Graph Representation Learning Graph Representation Learning D. Bacciu, F. Errica, A. Micheli, N. Navarin, L. Pasa, M. Podda, D. Zambon ....... Richness of Node Embeddings in Graph Echo State Networks D. Tortorella, A. Micheli ....................................................................................... An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features N. Navarin, L. Pasa, L. Oneto, A. Sperduti............................................................ Convolutional Transformer via Graph Embeddings for Few-shot Toxicity and Side Effect Prediction L. Torres, B. Ribeiro, J. Arrais .............................................................................. Hidden Markov Models for Temporal Graph Representation Learning F. Errica, A. Gravina, D. Bacciu, A. Micheli ........................................................ A Tropical View of Graph Neural Networks F. Landolfi, D. Bacciu, D. Numeroso .................................................................... Graph-based Categorical Embedding W. Wang, S. Bromuri, M. Dumontier ..................................................................... FouriER: Link Prediction by Mixing Tokens with Fourier-enhanced MetaFormer T. Vu, H. Ngo, B. Le, T. Le .................................................................................... Feature selection and dimension reduction Feature Selection for Concept Drift Detection F. Hinder, B. Hammer ........................................................................................... Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA) M. Biehl, S. Lövdal ................................................................................................ Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection M. Münch, K. S. Bohnsack, A. Engelsberger, F.-M. Schleif, T. Villmann ............. Improved the locally aligned ant technique (LAAT) strategy to recover manifolds embedded in strong noise F. Contreras, K. Bunte, R. Peletier ........................................................................ Nesterov momentum and gradient normalization to improve t-SNE convergence and neighborhood preservation, without early exaggeration P. Lambert, J. Lee, E. Couplet, C. de Bodt ............................................................ On Feature Removal for Explainability in Dynamic Environments F. Fumagalli, M. Muschalik, E. Hüllermeier, B. Hammer .................................... Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts V. Vaquet, J. Brinkrolf, B. Hammer ....................................................................... A Counterexample to Ockham's Razor and the Curse of Dimensionality: Marginalising Complexity and Dimensionality for GMMs B. Frénay ............................................................................................................... Feature Selection for Multi-label Classification with Minimal Learning Machine J. Linja, J. Hämäläinen, T. Kärkkäinen ............................................................... Learning with Boosting Decision Stumps for Feature Selection in Evolving Data Streams D. Nowak-Assis .................................................................................................... Towards Machine Learning Models that We Can Trust: Testing,Improving, and Explaining Robustness Towards Machine Learning Models that We Can Trust: Testing, Improving,and Explaining Robustness M. Pintor, A. Demontis, B. Biggio ....................................................................... Secure Federated Learning with Kernel Affine Hull Machines M. Kumar, B. Moser, L. Fischer .......................................................................... Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization G. Piras, G. Floris, R. Mura, L. Scionis, M. Pintor, B. Biggio, A. Demontis ...... On the Limitations of Model Stealing with Uncertainty Quantification Models D. Pape, S. Däubener, T. Eisenhofer, A. E. Cinà, L. Schönherr.......................... Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective L. Oneto, S. Ridella, D. Anguita .......................................................................... Single-pass uncertainty estimation with layer ensembling for regression: application to proton therapy dose prediction for head and neck cancer A. M. Barragan Montero, R. Tilman, M. Huet-Dastarac, J. Lee ......................... Fairness and Interpretability, Clustering, and NLP Mixture of stochastic block models for multiview clustering K. De Santiago, M. Szafranski, C. Ambroise ....................................................... Fine-tuning is not (always) overfitting artifacts J. Bogaert, E. Jean, C. de Bodt, F.-X. Standaert ................................................. On Instance Weighted Clustering Ensembles P. Moggridge, N. Helian, Y. Sun, M. Lilley ......................................................... Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis Z. Shi, A. Lipani ................................................................................................... Similarity versus Supervision: Best Approaches for HS Code Prediction S. Stassin, O. Amel, S. A. Mahmoudi, X. Siebert .................................................. Multimodal Approach for Harmonized System Code Prediction O. Amel, S. Stassin, S. A. Mahmoudi, X. Siebert .................................................. Mitigating Robustness Bias: Theoretical Results and Empirical Evidences D. Franco, L. Oneto, D. Anguita ......................................................................... End-to-End Neural Network Training for Hyperbox-Based Classification D. Martins, C. Lülf, F. Gieseke ............................................................................ TabSRA: An Attention based Self-Explainable Model for Tabular Learning K. M. Amekoe, M. D. Dilmi, H. Azzag, Z. Chelly Dagdia, M. Lebbah, G. Jaffre Improving Fairness via Intrinsic Plasticity in Echo State Networks A. Ceni, D. Bacciu, V. De Caro, C. Gallicchio, L. Oneto .................................... Is Boredom an Indicator on the way to Singularity of Artificial Intelligence? Hypotheses as Thought-Provoking Impulse M. Bogdan ........................................................................................................... Adversarial Auditing of Machine Learning Models under Compound Shift K. Bhanot, D. Wei, I. Baldini, K. Bennett ............................................................ Language Modeling in Logistics: Customer Calling Prediction X. Chen, G. Anerdi, D. Tan, S. Bromuri .............................................................. Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability I. Spinelli, M. Guerra, F. Bianchi, S. Scardapane ............................................... Quantum Artificial Intelligence Quantum Artificial Intelligence: A tutorial J. D. Martín-Guerrero, L. Lamata, T. Villmann .................................................. Quantum Feature Selection with Variance Estimation A. Poggiali, A. Bernasconi, A. Berti, G. Del Corso, R. Guidotti ......................... Logarithmic Quantum Forking A. Berti ................................................................................................................. Quantum-ready vector quantization: Prototype learning as a binary optimization problem A. Engelsberger, T. Villmann .............................................................................. Potential analysis of a Quantum RL controller in the context of autonomous driving M. L. Hickmann, A. Raulf, F. Köster, F. Schwenker, H.-M. Rieser ..................... Green Machine Learning Green Machine Learning V. Bolón-Canedo, L. Morán-Fernández, B. Cancela, A. Alonso-Betanzos ......... Logarithmic division for green feature selection: an information-theoretic approach S. Suárez-Marcote, L. Morán-Fernández, V. Bolón-Canedo ............................... Efficient feature selection for domain adaptation using Mutual Information Maximization G. Castillo García, L. Morán-Fernández, V. Bolón-Canedo............................... Automated green machine learning for condition-based maintenance A. Lourenco, C. Ferraz, J. Meira, G. Marreiros, V. Bolón-Canedo, A. Alonso-Betanzos .............................................................................................. Multispectral Texture Classification in Agriculture M. Shumska, K. Bunte .......................................................................................... Reinforcement learning and Evolutionary computation DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety A. Correia, L. Alexandre...................................................................................... Automatic Trade-off Adaptation in Offline RL P. Swazinna, S. Udluft, T. Runkler....................................................................... Enhancing Evolution Strategies with Evolution Path Bias O. Kramer ............................................................................................................ Multi-Fidelity Reinforcement Learning with Control Variates S. Khairy, P. Balaprakash ................................................................................... Sun Tracking using a Weightless Q-Learning Neural Network G. Souza, P. Lima, F. França .............................................................................. A model-based approach to meta-Reinforcement Learning: Transformers and tree search B. Pinon, R. Jungers, J.-C. Delvenne................................................................... Derivative-Free Optimization Approaches for Force Polytopes Prediction G. Laisné, N. Rezzoug, J.-M. Salotti .................................................................... Policy-Based Reinforcement Learning in the Generalized Rock-Paper-Scissors Game I. G. Mali, G. Czibula .......................................................................................... Classification Performance Evaluation of Activation Functions in Extreme Learning Machine K. Struniawski, A. Konopka, R. Kozera ............................................................... Evaluating Curriculum Learning Strategies for Pancreatic Cancer Prediction E. Mosqueira-Rey, D. Vázquez-Lema, E. Hernández-Pereira ............................. Improving the DRASiW performance by exploiting its own "Mental Images" G. Coda, M. De Gregorio, A. Sorgente, P. Vanacore .......................................... Efficient Knowledge Aggregation Methods for Weightless Neural Networks O. Napoli, A. M. de Almeida, J. M. S. Dias, L. B. Rosário, E. Borin, M. Breternitz Jr.................................................................................................... Learning Vector Quantization in Context of Information Bottleneck Theory M. Mohannazadeh Bakhtiari, D. Staps, T. Villmann ........................................... SOM-based Classification and a Novel Stopping Criterion for Astroparticle Applications L. Sanchez, E. Merényi, C. Tunnell ..................................................................... WiSARD-based Ensemble Learning L. Lusquino Filho, F. França, P. Lima ................................................................ Deep learning and Computer vision Entropy Based Regularization Improves Performance in the Forward-Forward Algorithm M. Pardi, D. Tortorella, A. Micheli ..................................................................... On the number of latent representations in deep neural networks for tabular data E. Couplet, P. Lambert, M. Verleysen, J. Lee, C. de Bodt ................................... CRE: Circle relationship embedding of patches in vision transformer Z. Yu, J. Triesch ................................................................................................... Introducing Convolutional Channel-wise Goodness in Forward-Forward Learning A. Papachristodoulou, C. Kyrkou, S. Timotheou, T. Theocharides ..................... An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection H. Daglayan, S. Vary, P.-A. Absil ........................................................................ On Transformer Autoregressive Decoding for Multivariate Time Series Forecasting M. Aldosari, J. Miller .......................................................................................... Don't waste SAM N. Abou Baker, U. Handmann ............................................................................. Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis F. Richert, M. Straat, E. Oostwal, M. Biehl ......................................................... Real-time Detection of Evoked Potentials by Deep Learning: a Case Study L. Amato, M. Maschietto, A. Leparulo, M. Tambaro, S. Vassanelli, A. Sperduti ........................................................................................................... Coordinate descent on the Stiefel manifold for deep neural network training E. Massart, V. Abrol ............................................................................................ Action-Based ADHD Diagnosis in Video Y. Li, Y. Yang, R. Nair, M. Naqvi ......................................................................... Hierarchical priors for Hyperspherical Prototypical Networks S. Fonio, L. Paletto, M. Cerrato, D. Ienco, R. Esposito ...................................... Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements H. Schneider, D. Biesner, A. Ashokan, M. Broß, R. Kador, S. Halscheidt, G. Bagyo, P. Dankerl, H. Ragab, J. Yamamura, C. Labisch, R. Sifa................... Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture H. boudegga, Y. Elloumi, A. Ben Abdallah, R. Kachouri, M. H. Bedoui ............. Graph for Transformer Feature: A New Approach for Face Anti-Spoofing Q.-H. Trinh, H. Nguyen, V. Nguyen, X.-M. Nguyen, H.-D. Nguyen .................... Temporal Ensembling-based Deep k-Nearest Neighbours for Learning with Noisy Labels A.-I. Albu ............................................................................................................. Evaluation of Contrastive Learning for Electronic Component Detection L. Silva, A. Freire, B. Fernandes, G. Azevedo, S. Oliveira .................................. Sequential data, and Meta-learning Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes T. Bosser, S. Ben Taieb ........................................................................................ A Protocol for Continual Explanation of SHAP A. Cossu, F. Spinnato, R. Guidotti, D. Bacciu ..................................................... Residual Reservoir Computing Neural Networks for Time-series Classification C. Gallicchio, A. Ceni .......................................................................................... Probabilistic Adaptation for Meta-Learning T. Adel.................................................................................................................. A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. F. Dama, C. Sinoquet, C. Lejus-Bourdeau .......................................................... Deep dynamic co-clustering of streams of count data: a new online Zip-dLBM G. Marchello, M. Corneli, C. Bouveyron ............................................................ Communication-Efficient Ridge Regression in Federated Echo State Networks V. De Caro, A. Di Mauro, D. Bacciu, C. Gallicchio ........................................... Simultaneous failures classification in a predictive maintenance case A. Hubermont, E. tuci, N. De Quattro ................................................................. Hybrid modelling of dynamic anaerobic digestion process in full-scale with LSTM NN and BMP measurements A. Meola, S. Weinrich .......................................................................................... Wind Power Prediction with ETSformer O. Kramer, J. Baumann ....................................................................................... Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? R. Egele, I. Guyon, Y. Sun, P. Balaprakash ......................................................... Machine Learning Applied to Sign Language Trends and Challenges for Sign Language Recognition with Machine Learning J. Fink, M. De Coster, J. Dambre, B. Frénay ...................................................... Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions F. Nunnari, A. Rios, U. Reichel, C. Bhuvaneshwara, P. Filntisis, P. Maragos, F. Burkhardt, F. Eyben, B. Schuller, S. Ebling .................................................... Exploring Strategies for Modeling Sign Language Phonology L. Kezar, T. Srinivasan, R. Carlin, J. Thomason, Z. Sevcikova Sehyr, N. Caselli ............................................................................................................. Exploring the Importance of Sign Language Phonology for a Deep Neural Network J. Martinez Rodriguez, M. Larson, L. ten Bosch ................................................. Large-scale dataset and benchmarking for hand and face detection focused on sign language A. L. Cavalcante Carneiro, D. H. Pinheiro Salvadeo, L. Brito Silva .................. Disambiguating Signs: Deep Learning-based Gloss-level Classification for German Sign Language by Utilizing Mouth Actions D. N. Pham, V. Czehmann, E. Avramidis ............................................................ Efficient Learning in Spiking Neural Networks Efficient Learning in Spiking Models A. Rast, M. A. Aoun, E. Elia, N. Crook ................................................................ Spiking neural networks with Hebbian plasticity for unsupervised representation learning N. B. Ravichandran, A. Lansner, P. Herman ....................................................... Functional Resonant Synaptic Clusters for Decoding Time-Structured Spike Trains N. Crook, A. Rast, E. Elia, M. A. Aoun ................................................................ Pattern Recognition Spiking Neural Network for Classification of Chinese Characters N. Russo, W. Yuzhong, T. Madsen, K. Nikolic ..................................................... Energy-efficient detection of a spike sequence L. Le Coeur, N. Riedman, S. Sarup, K. Boahen ................................................... Anomaly Detection, and Learning Algorithms Anomaly detection in irregular image sequences for concentrated solar power plants S. Patra, T. K. H. Le, S. Ben Taieb ...................................................................... Knowledge Distillation for Anomaly Detection A. A. Pol, E. Govorkova, S. Gronroos, N. Chernyavskaya, P. Harris, M. Pierini, I. Ojalvo, P. Elmer............................................................................. Comparative study of the synfire chain and ring attractor model for timing in the premotor nucleus in male Zebra Finches F. Hyseni, N. Rougier, A. Leblois ........................................................................ Don't skip the skips: autoencoder skip connections improve latent representation discrepancy for anomaly detection A.-S. Collin, C. de Bodt, D. Mulders, C. De Vleeschouwer ................................. Variants of Neural Gas for Regression Learning T. Villmann, R. Schubert, M. Kaden .................................................................... Hybrid Deep Learning-Based Air and Water Quality Prediction Model J. Yoon, D. Yu, Y. lee ........................................................................................... Sleep analysis in a CLIS patient using soft-clustering: a case study S. Adama, M. Bogdan .......................................................................................... FairBayRank: A Fair Personalized Bayesian Ranker A. Noulapeu Ngaffo, J. Albert, B. Frénay, G. Perrouin ....................................... Robust and Cheap Safety Measure for Exoskeletal Learning Control with Estimated Uniform PAC (EUPAC) F. Weiske, J. Jäkel ............................................................................................... 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