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ORCID28th 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, Priscila M. V. Lima:
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, Nicolò Navarin, Alessandro Sperduti:
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, Alexander Schulz:
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, Priscila M. V. Lima:
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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