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28th ESANN 2020: Bruges, Belgium
- 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020, Bruges, Belgium, October 2-4, 2020. 2020
Adversarial learning, robustness and fairness
- István Megyeri, István Hegedüs, Márk Jelasity:
Attacking Model Sets with Adversarial Examples. 1-6 - Julia Lust, Alexandru Paul Condurache:
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples. 7-12 - Magda Friedjungová, Daniel Vasata
, Tomás Chobola, Marcel Jirina:
Unsupervised Latent Space Translation Network. 13-18 - André Artelt, Barbara Hammer:
Efficient computation of counterfactual explanations of LVQ models. 19-24 - Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff:
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks. 25-30 - Luca Oneto, Nicolò Navarin, Michele Donini:
Learning Deep Fair Graph Neural Networks. 31-36 - Aluizio Lima Filho, Gabriel P. Guarisa, Leopoldo Lusquino Filho, Luiz F. R. Oliveira, Carlos Alberto Nunes Cosenza, Felipe M. G. França
Interpretation of Model Agnostic Classifiers via Local Mental Images. 37-42 - Céline Beji, Eric Benhamou, Michaël Bon, Florian Yger, Jamal Atif:
Estimating Individual Treatment Effects through Causal Populations Identification. 43-48 - Tiago A. O. Alves, Sandip Kundu:
Towards Adversarial Attack Resistant Deep Neural Networks. 49-54 - Pawel Morawiecki, Przemyslaw Spurek, Marek Smieja, Jacek Tabor:
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models. 55-60 - Aymen Cherif, Hugo Serieys:
Adversarial domain adaptation without gradient reversal layer. 61-66
Image and signal processing, matrix computations and topological data
- Abolfazl Taghribi, Kerstin Bunte, Michele Mastropietro, Sven De Rijcke, Peter Tiño:
ASAP - A Sub-sampling Approach for Preserving Topological Structures. 67-72 - Cécile Hautecoeur, François Glineur:
Image completion via nonnegative matrix factorization using B-splines. 73-78 - Hafez Farazi, Sven Behnke:
Motion Segmentation using Frequency Domain Transformer Networks. 79-84 - Marc M. Van Hulle, Bob Van Dyck, Benjamin Wittevrongel, Flavio Camarrone, Ine Dauwe, Evelien Carrette, Alfred Meurs, Paul Boon, Dirk Van Roost:
Predicting low gamma- from lower frequency band activity in electrocorticography. 85-90 - Julien Dewez, François Glineur:
Lower bounds on the nonnegative rank using a nested polytopes formulation. 91-96
Image and signal processing, matrix computations and topological data
- Clara Gainon de Forsan de Gabriac, Constance Scherer, Amina Djelloul, Vincent Guigue, Patrick Gallinari:
Resume: A Robust Framework for Professional Profile Learning & Evaluation. 97-102 - Matthias Rath, Alexandru Paul Condurache:
Invariant Integration in Deep Convolutional Feature Space. 103-108 - Georgi Angelov, Bogdan Georgiev:
On Learning a Control System without Continuous Feedback. 109-114 - Perrine Cribier-Delande, Raphaël Puget, Vincent Guigue, Ludovic Denoyer:
Time Series Prediction using Disentangled Latent Factors. 115-120 - Marco Podda, Alessio Micheli, Davide Bacciu, Paolo Milazzo:
Biochemical Pathway Robustness Prediction with Graph Neural Networks. 121-126 - Niccolò Pancino, Alberto Rossi, Giorgio Ciano, Giorgia Giacomini, Simone Bonechi, Paolo Andreini, Franco Scarselli, Monica Bianchini, Pietro Bongini:
Graph Neural Networks for the Prediction of Protein-Protein Interfaces. 127-132 - Alberto Rossi, Markus Hagenbuchner, Franco Scarselli, Ah Chung Tsoi:
Embedding of FRPN in CNN architecture. 133-138 - Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid:
Verifying Deep Learning-based Decisions for Facial Expression Recognition. 139-144 - Oscar J. Pellicer-Valero, María J. Rupérez-Moreno, José David Martín-Guerrero:
Cost-free resolution enhancement in Convolutional Neural Networks for medical image segmentation. 145-150 - Nicolò Navarin, Wolfgang Erb, Luca Pasa, Alessandro Sperduti:
Linear Graph Convolutional Networks. 151-156 - Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Deep Recurrent Graph Neural Networks. 157-162 - Brian Maguire, Faisal Ghaffar:
Investigating 3D-STDenseNet for Explainable Spatial Temporal Crime Forecasting. 163-168 - Carlos M. Alaíz, Ángela Fernández, José R. Dorronsoro:
Visualization of the Feature Space of Neural Networks. 169-174 - Federico Errica, Davide Bacciu, Alessio Micheli:
Theoretically Expressive and Edge-aware Graph Learning. 175-180 - Robin Condat, Alexandrina Rogozan, Abdelaziz Bensrhair:
Random Signal Cut for Improving Multimodal CNN Robustness of 2D Road Object Detection. 181-186 - Doreen Jirak, Stefan Wermter:
New Results on Sparse Autoencoders for Posture Classification and Segmentation. 187-192 - Nicolas Boria, Benjamin Négrevergne, Florian Yger:
Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent. 193-198 - Iulian-Ionut Felea, Radu Dogaru:
Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms. 199-204 - Guilherme G. P. Freitas Pires, Mário A. T. Figueiredo:
Variational MIxture of Normalizing Flows. 205-210 - Alan T. L. Bacellar, Brunno F. Goldstein, Victor da Cruz Ferreira, Leandro Santiago, Priscila M. V. Lima, Felipe M. G. França:
Fast Deep Neural Networks Convergence using a Weightless Neural Model. 211-216 - Sharan Yalburgi, Tirtharaj Dash, Ramya Hebbalaguppe, Srinidhi Hegde, Ashwin Srinivasan:
An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression. 217-222 - Mohammadreza Qaraei, Sujay Khandagale, Rohit Babbar:
Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification. 223-228 - Manon Flageat, Kai Arulkumaran, Anil A. Bharath:
Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control. 229-234 - Marie Chavent, Jérôme Lacaille, Alex Mourer, Madalina Olteanu:
Sparse K-means for mixed data via group-sparse clustering. 235-240
Machine Learning Applied to Computer Networks
- Alexander Gepperth, Sebastian Rieger:
A Survey of Machine Learning applied to Computer Networks. 241-250 - Vladimir Muliukha, Alexey Lukashin, Lev V. Utkin, Mikhail Popov, Anna A. Meldo:
Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System. 251-256
Quantum Machine Learning
- José David Martín-Guerrero, Lucas Lamata:
Quantum Machine Learning. 257-266 - Archismita Dalal, Eduardo J. Páez, Seyed Shakib Vedaie, Barry C. Sanders:
Machine learning framework for control in classical and quantum domains. 267-272 - Przemyslaw R. Grzybowski, Gorka Muñoz-Gil, Alejandro Pozas-Kerstjens, Miguel Ángel García-March, Maciej Lewenstein:
Understanding and improving unsupervised training of Boltzman machines. 273-278 - Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden:
Quantum-Inspired Learning Vector Quantization for Classification Learning. 279-284 - Daniele Bajoni, Dario Gerace, Chiara Macchiavello, Francesco Tacchino, Panagiotis Kl. Barkoutsos, Ivano Tavernelli:
An quantum algorithm for feedforward neural networks tested on existing quantum hardware. 285-290 - Sebastian Feld, Christoph Roch, Katja Geirhos, Thomas Gabor:
Approximating Archetypal Analysis Using Quantum Annealing. 292-296 - Lukas Franken, Bogdan Georgiev:
Explorations in Quantum Neural Networks with Intermediate Measurements. 297-302
Recurrent networks and reinforcement learning
- Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch, Thomas Scholten, Volker Wulfmeyer, Martin V. Butz:
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction. 303-308 - Lucas Vos, Twan van Laarhoven:
Softmax Recurrent Unit: A new type of RNN cell. 309-314 - Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latré:
Language Grounded Task-Adaptation in Reinforcement Learning. 315-320 - Moritz Wolter, Angela Yao, Sven Behnke:
Object-centered Fourier Motion Estimation and Segment-Transformation Prediction. 321-326 - Markus Roland Ernst, Jochen Triesch, Thomas Burwick:
Recurrent Feedback Improves Recognition of Partially Occluded Objects. 327-332 - Marius Hobbhahn, Martin V. Butz, Sarah Fabi, Sebastian Otte:
Sequence Classification using Ensembles of Recurrent Generative Expert Modules. 333-338 - Hannes Eriksson, Christos Dimitrakakis:
Epistemic Risk-Sensitive Reinforcement Learning. 339-344 - Tim Cofala, Lars Elend, Oliver Kramer:
Tournament Selection Improves Cartesian Genetic Programming for Atari Games. 345-350 - Michel Tokic, Anja von Beuningen, Christoph Tietz, Hans-Georg Zimmermann:
Handling missing data in recurrent neural networks for air quality forecasting. 351-356
Unsupervised learning
- Hervé Frezza-Buet:
Self-organizing maps in manifolds with complex topologies: An application to the planning of closed path for indoor UAV patrols. 357-362 - Florian Mirus, Terrence C. Stewart, Jörg Conradt:
Detection of abnormal driving situations using distributed representations and unsupervised learning. 363-368 - Sara Kaczynska, Rebecca Marion, Rainer von Sachs:
Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation. 369-374
Feature selection and dimensionality reduction
- Johannes Brinkrolf, Barbara Hammer:
Sparse Metric Learning in Prototype-based Classification. 375-380 - Victor Hamer, Pierre Dupont:
Joint optimization of predictive performance and selection stability. 381-386 - Francesco Crecchi, Cyril de Bodt, Michel Verleysen, John A. Lee, Davide Bacciu:
Perplexity-free Parametric t-SNE. 387-392 - Adrien Bibal, Viet Minh Vu, Géraldin Nanfack, Benoît Frénay:
Explaining t-SNE Embeddings Locally by Adapting LIME. 393-398 - Laura Morán-Fernández, Verónica Bolón-Canedo, Amparo Alonso-Betanzos:
Do we need hundreds of classifiers or a good feature selection? 399-404 - Moritz Heusinger, Frank-Michael Schleif
Random Projection in supervised non-stationary environments. 405-410 - Federico Amato, Fabian Guignard, Philippe Jacquet, Mikhail F. Kanevski:
On Feature Selection Using Anisotropic General Regression Neural Network. 411-416
Statistical learning and optimization
- Alexandru Onose, Seyed Iman Mossavat, Henk-Jan H. Smilde:
A preconditioned accelerated stochastic gradient descent algorithm. 417-422 - Luca Oneto, Sandro Ridella, Davide Anguita:
Improving the Union Bound: a Distribution Dependent Approach. 423-428 - Vincent Schellekens, Laurent Jacques:
Compressive Learning of Generative Networks. 429-434 - Oliver Kramer:
Learning Step Size Adaptation in Evolution Strategies. 435-440
Tensor Decompositions in Deep Learning
- Davide Bacciu, Danilo P. Mandic:
Tensor Decompositions in Deep Learning. 441-450 - Daniele Castellana, Davide Bacciu:
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data. 451-456 - Swarup Ranjan Behera, Vijaya Saradhi Vedula:
Mining Temporal Changes in Strengths and Weaknesses of Cricket Players Using Tensor Decomposition. 457-462
Image and text analysis
- Yi Zhao, Nils Wandel, Magdalena Landl, Andrea Schnepf, Sven Behnke:
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution. 463-468 - Salla Aario, Ajinkya Gorad, Miika Arvonen, Simo Särkkä:
Respiratory Pattern Recognition from Low-Resolution Thermal Imaging. 469-474 - Ricardo Cardoso Pereira, Joana Cristo Santos, José Pereira Amorim, Pedro Pereira Rodrigues, Pedro Henriques Abreu:
Missing Image Data Imputation using Variational Autoencoders with Weighted Loss. 475-480 - Guillaume Le Berre, Christophe Cerisara:
Seq-to-NSeq model for multi-summary generation. 481-486 - Janderson Ferreira, Agostinho A. F. Júnior, Yves M. Galvão, Bruno J. T. Fernandes, Pablo V. A. Barros:
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning. 487-492
Learning from partially labeled data
- Siamak Mehrkanoon, Xiaolin Huang, Johan A. K. Suykens:
Learning from partially labeled data. 493-502 - Bernardo Pérez Orozco, Stephen J. Roberts:
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks. 503-508 - Christoph Raab, Peter Meier, Frank-Michael Schleif
Domain Invariant Representations with Deep Spectral Alignment. 509-514 - Robin Vogel, Mastane Achab, Stéphan Clémençon, Charles Tillier:
Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling. 515-520 - Kiki van der Heijden, Siamak Mehrkanoon:
Modelling human sound localization with deep neural networks. 521-526 - Fan He, Sanli Tang, Siamak Mehrkanoon, Xiaolin Huang, Jie Yang:
A Real-time PCB Defect Detector Based on Supervised and Semi-supervised Learning. 527-532
Machine learning in the pharmaceutical industry
- Gaël de Lannoy, Thibault Helleputte, Paul Smyth:
Machine learning in the biopharma industry. 533-540 - Paul Smyth, John A. Lee, Gaël de Lannoy, Thomas Beznik:
Deep Learning to Detect Bacterial Colonies for the Production of Vaccines. 541-546 - Davide Rigoni
A Systematic Assessment of Deep Learning Models for Molecule Generation. 547-552 - Paul Smyth, Tanguy Naets, Gaël de Lannoy, Laurent Sorber:
An agile machine learning project in pharma - developing a Mask R-CNN-based web application for bacterial colony counting. 553-558
Frontiers in Reservoir Computing
- Claudio Gallicchio, Mantas Lukosevicius, Simone Scardapane:
Frontiers in Reservoir Computing. 559-566 - Benjamin Paassen
Reservoir memory machines. 567-572 - Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli:
Pyramidal Graph Echo State Networks. 573-578 - Claudio Gallicchio, Alessio Micheli, Antonio Sisbarra:
Simplifying Deep Reservoir Architectures. 579-584 - Benedikt Vettelschoss, Matthias Freiberger, Joni Dambre:
Self-organized dynamic attractors in recurrent neural networks. 585-590 - Gin Chong Lee, Chu Kiong Loo, Wei Shiung Liew, Stefan Wermter:
Self-Organizing Kernel-based Convolutional Echo State Network for Human Actions Recognition. 591-596
Language processing in the era of deep learning
- Ivano Lauriola, Alberto Lavelli, Fabio Aiolli:
Language processing in the era of deep learning. 597-606 - Katya Kudashkina, Peter Wittek, Jamie Kiros, Graham W. Taylor:
Modular Length Control for Sentence Generation. 607-612 - Farrokh Mehryary, Hans Moen, Tapio Salakoski, Filip Ginter:
Entity-Pair Embeddings for Improving Relation Extraction in the Biomedical Domain. 613-618 - Nils Worzyk, Stefan Niewerth, Oliver Kramer:
Adversarials-1 in Speech Recognition: Detection and Defence. 619-624 - Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq:
On the long-term learning ability of LSTM LMs. 625-630 - György Kovács, Rickard Brännvall, Johan Öhman, Marcus Liwicki:
Cross-Encoded Meta Embedding towards Transfer Learning. 631-636 - Ivano Lauriola, Stefano Campese, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli:
Exploring the feature space of character-level embeddings. 637-642
Supervised learning
- Pedro Xavier, Massimo De Gregorio, Felipe M. G. França
Detection of elementary particles with the WiSARD n-tuple classifier. 643-648 - Peter Bellmann, Patrick Thiam, Friedhelm Schwenker:
Automatic Pain Intensity Recognition: Training Set Selection based on Outliers and Centroids. 649-654 - George O. de A. Azevedo, Leandro H. de S. Silva, Agostinho A. F. Júnior, Bruno J. T. Fernandes, Sergio C. Oliveira:
Binary and Multi-label Defect Classification of Printed Circuit Board based on Transfer Learning. 655-660 - Alexander Hartl, Félix Iglesias, Tanja Zseby:
SDOstream: Low-Density Models for Streaming Outlier Detection. 661-666 - Jan Philip Göpfert, Heiko Wersing, Barbara Hammer:
Locally Adaptive Nearest Neighbors. 667-672 - Matilde Tristany, Sérgio Pequito, Pedro A. Santos, Mário A. T. Figueiredo:
Equilibrium Propagation for Complete Directed Neural Networks. 673-678 - Lode Vuegen, Peter Karsmakers:
On-edge adaptive acoustic models: an application to acoustic person presence detection. 679-684 - Georgios Birpoutsoukis, Julien M. Hendrickx:
Gaussian process regression for the estimation of stable univariate time-series processes. 685-690 - Joonas Hämäläinen, Tommi Kärkkäinen:
Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression. 691-696 - Tossapol Pomsuwan, Alex Alves Freitas:
Adapting Random Forests to Cope with Heavily Censored Datasets in Survival Analysis. 697-702 - Fabian Guignard, Mohamed Laib, Mikhail F. Kanevski:
Model Variance for Extreme Learning Machine. 703-708 - Neta Rabin:
Multi-Directional Laplacian Pyramids for Completion of Missing Data Entries. 709-714 - Aparajit Narayan, Elio Tuci, William Xachiti, Aaron Parsons:
Navigational Freespace Detection for Autonomous Driving in Fixed Routes. 715-720 - Eric Benhamou, David Saltiel:
Similarities between policy gradient methods in reinforcement and supervised learning. 721-726
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