- 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. ESANN 2023 - Felipe Contreras, Kerstin Bunte, Reynier Peletier:
Improved the locally aligned ant technique (LAAT) strategy to recover manifolds embedded in strong noise. ESANN 2023 - André Correia, Luís A. Alexandre:
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety. ESANN 2023 - Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu:
A Protocol for Continual Explanation of SHAP. ESANN 2023 - 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. ESANN 2023 - Nigel T. Crook, Alex Rast, Eleni Elia, Mario Antoine Aoun:
Functional Resonant Synaptic Clusters for Decoding Time-Structured Spike Trains. ESANN 2023 - Hazan Daglayan, Simon Vary, Pierre-Antoine Absil:
An Alternating Minimization Algorithm with Trajectory for Direct Exoplanet Detection. ESANN 2023 - 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. ESANN 2023 - Romain Egele, Isabelle Guyon, Yixuan Sun, Prasanna Balaprakash:
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization? ESANN 2023 - Alexander Engelsberger, Thomas Villmann:
Quantum-ready vector quantization: Prototype learning as a binary optimization problem. ESANN 2023 - Federico Errica, Alessio Gravina, Davide Bacciu, Alessio Micheli:
Hidden Markov Models for Temporal Graph Representation Learning. ESANN 2023 - Leopoldo Lusquino Filho, Felipe M. G. França, Priscila Lima:
WiSARD-based Ensemble Learning. ESANN 2023 - Jérôme Fink, Mathieu De Coster, Joni Dambre, Benoît Frénay:
Trends and Challenges for Sign Language Recognition with Machine Learning. ESANN 2023 - Samuele Fonio, Lorenzo Paletto, Mattia Cerrato, Dino Ienco, Roberto Esposito:
Hierarchical priors for Hyperspherical Prototypical Networks. ESANN 2023 - Danilo Franco, Luca Oneto, Davide Anguita:
Mitigating Robustness Bias: Theoretical Results and Empirical Evidences. ESANN 2023 - Benoît Frénay:
A Counterexample to Ockham's Razor and the Curse of Dimensionality: Marginalising Complexity and Dimensionality for GMMs. ESANN 2023 - Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer:
On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 - Claudio Gallicchio, Andrea Ceni:
Residual Reservoir Computing Neural Networks for Time-series Classification. ESANN 2023 - Guillermo Castillo García, Laura Morán-Fernández, Verónica Bolón-Canedo:
Efficient feature selection for domain adaptation using Mutual Information Maximization. ESANN 2023 - Mali Imre Gergely, Gabriela Czibula:
Policy-Based Reinforcement Learning in the Generalized Rock-Paper-Scissors Game. ESANN 2023 - 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. ESANN 2023 - Fabian Hinder, Barbara Hammer:
Feature Selection for Concept Drift Detection. ESANN 2023 - Antoine Hubermont, Elio Tuci, Nicola De Quattro:
Simultaneous failures classification in a predictive maintenance case. ESANN 2023 - 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. ESANN 2023 - Lee Kezar, Tejas Srinivasan, Riley Carlin, Jesse Thomason, Zed Sevcikova Sehyr, Naomi Caselli:
Exploring Strategies for Modeling Sign Language Phonology. ESANN 2023 - Sami Khairy, Prasanna Balaprakash:
Multi-Fidelity Reinforcement Learning with Control Variates. ESANN 2023 - Gautier Laisné, Nasser Rezzoug, Jean Marc Salotti:
Derivative-Free Optimization Approaches for Force Polytopes Prediction. ESANN 2023 - 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. ESANN 2023 - Francesco Landolfi, Davide Bacciu, Danilo Numeroso:
A Tropical View of Graph Neural Networks. ESANN 2023 - Yichun Li, Yuxing Yang, Rajesh Nair, Syed Mohsen Naqvi:
Action-Based ADHD Diagnosis in Video. ESANN 2023