<TitleType>01</TitleType> <TitleText textcase="01">ESANN 2020 - Proceedings</TitleText> <Subtitle textcase="01">28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (online event)</Subtitle>

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COM.ONIXSUITE.9782875870742 03 02 i6doc 01 SKU 100656PDF 02 2875870742 03 9782875870742 15 9782875870742 DG 1 002 <TitleType>01</TitleType> <TitleText textcase="01">ESANN 2020 - Proceedings</TitleText> <Subtitle textcase="01">28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (online event)</Subtitle> 01 GCOI 28001100609140 1 A32 04 1 01 eng 07 752 03 25 1 TEC000000 T 01 06 01 <InvalidTag http-equiv="Content-Type" content="text/html; charset=utf-8" /> 03 <InvalidTag http-equiv="Content-Type" content="text/html; charset=utf-8" /> 01 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. Each year, around 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 2020 conference follows this tradition, while continuously adapting its scope to the new developments in the field. 03 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. Each year, around 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 2020 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 <invalidtag content="text/html; charset=utf-8" http-equiv="Content-Type"></invalidtag> Adversarial learning, robustness and fairness Attacking Model Sets with Adversarial Examples I. Megyeri, I. Hegedűs, M. Jelasity..........................................................................p. 1 GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples J. Lust, A. P. Condurache........................................................................................p. 7 Unsupervised Latent Space Translation Network M. Friedjungová, D. Vašata, T. Chobola, M. Jiřina..............................................p. 13 Efficient computation of counterfactual explanations of LVQ models A. Artelt, B. Hammer .............................................................................................p. 19 MultiMBNN: Matched and Balanced Causal Inference with Neural Networks A. Sharma, G. Gupta, R. Prasad, A. Chatterjee, L. Vig, G. Shroff ........................p. 25 Learning Deep Fair Graph Neural Networks L. Oneto, N. Navarin, M. Donini ........................................................................... p. 31 Interpretation of Model Agnostic Classifiers via Local Mental Images A. Lima Filho, G. Guarisa, L. Lusquino, L. Oliveira, C. Cosenza, F. França, P. Lima ..................................................................................................................p. 37 Estimating Individual Treatment Effects through Causal Populations Identification C. Beji, E. Benhamou, M. Bon, F. Yger, J. Atif......................................................p. 43 Towards Adversarial Attack Resistant Deep Neural Networks T. Alves, S. Kundu..................................................................................................p. 49 Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models P. Morawiecki, P. Spurek, M. Śmieja, J. Tabor.....................................................p. 55 Adversarial domain adaptation without gradient reversal layer A. Cherif, H. Serieys .............................................................................................. p. 61 Image and signal processing, matrix computations and topological data ASAP - A Sub-sampling Approach for Preserving Topological Structures A. Taghribi, K. Bunte, M. Mastropietro, S. De Rijcke, P. Tino .............................p. 67 Image completion via nonnegative matrix factorization using B-splines C. Hautecoeur, F. Glineur.....................................................................................p. 73 Motion Segmentation using Frequency Domain Transformer Networks H. Farazi, S. Behnke..............................................................................................p. 79 Predicting low gamma- from lower frequency band activity inelectrocorticography M. Van Hulle, B. Van Dyck, W. Benjamin, F. Camarrone, I. Dauwe, E. Carrette, A. Meurs, P. Boon, D. Van Roost.......................................................p. 85 Lower bounds on the nonnegative rank using a nested polytopes formulation J. Dewez, F. Glineur..............................................................................................p. 91 Deep learning and graph neural networks Resume: A Robust Framework for Professional Profile Learning & Evaluation C. Gainon de Forsan de Gabriac, C. Scherer, A. Djelloul, V. Guigue, P. Gallinari............................................................................................................p. 97 Invariant Integration in Deep Convolutional Feature Space M. Rath, A. P Condurache...................................................................................p. 103 On Learning a Control System without Continuous Feedback G. Angelov, B. Georgiev ...................................................................................... p. 109 Time Series Prediction using Disentangled Latent Factors P. Cribier-Delande, R. Puget, V. Guigue, L. Denoyer.........................................p. 115 Biochemical Pathway Robustness Prediction with Graph Neural Networks M. Podda, A. Micheli, D. Bacciu, P. Milazzo......................................................p. 121 Graph Neural Networks for the Prediction of Protein-Protein Interfaces N. Pancino, A. Rossi, G. Ciano, G. Giacomini, S. Bonechi, P. Andreini, F. Scarselli, M. Bianchini, P. Bongini.................................................................p. 127 Embedding of FRPN in CNN architecture A. Rossi, M. Hagenbuchner, F. Scarselli, A. C. Tsoi...........................................p. 133 Verifying Deep Learning-based Decisions for Facial Expression Recognition I. Rieger, R. Kollmann, B. Finzel, D. Seuss, U. Schmid.......................................p. 139 Cost-free resolution enhancement in Convolutional Neural Networks for medical image segmentation O. J. Pellicer Valero, M. J. Rupérez-Moreno, J. D. Martín-Guerrero ................p. 145 Linear Graph Convolutional Networks N. Navarin, W. Erb, L. Pasa, A. Sperduti ............................................................ p. 151 Deep Recurrent Graph Neural Networks L. Pasa, N. Navarin, A. Sperduti .........................................................................p. 157 Investigating 3D-STDenseNet for Explainable Spatial Temporal Crime Forecasting B. Maguire, F. Ghaffar........................................................................................p. 163 Visualization of the Feature Space of Neural Networks C. M. Alaíz, A. Fernández, J. R. Dorronsoro ......................................................p. 169 Theoretically Expressive and Edge-aware Graph Learning F. Errica, D. Bacciu, A. Micheli..........................................................................p. 175 Random Signal Cut for Improving Multimodal CNN Robustness of 2D Road Object Detection R. Condat, A. Rogozan, A. Bensrhair ..................................................................p. 181 New Results on Sparse Autoencoders for Posture Classification and Segmentation D. Jirak, S. Wermter ............................................................................................p. 187 Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent N. Boria, B. Negrevergne, F. Yger.......................................................................p. 193 Improving Light-weight Convolutional Neural Networks for Face Recognition Targeting Resource Constrained Platforms I.-I. Felea, R. Dogaru .......................................................................................... p. 199 Variational MIxture of Normalizing Flows G. Pires, M. Figueiredo.......................................................................................p. 205 Fast Deep Neural Networks Convergence using a Weightless Neural Model A. T. L. Bacellar, B. F. Goldstein, V. C Ferreira, L. Santiago, P. Lima, F. França.............................................................................................................p. 211 An Empirical Study of Iterative Knowledge Distillation for Neural Network Compression S. Yalburgi, T. Dash, R. Hebbalaguppe, S. Hegde, A. Srinivasan ....................... p. 217 Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification M. Qaraei, S. Khandagale, R. Babbar.................................................................p. 223 Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control M. Flageat, K. Arulkumaran, A. A. Bharath........................................................p. 229 Sparse K-means for mixed data via group-sparse clustering M. Chavent, J. Lacaille, A. Mourer, M. Olteanu ................................................. p. 235 Machine Learning Applied to Computer Networks A Survey of Machine Learning applied to Computer Networks A. Gepperth, S. Rieger ......................................................................................... p. 241 Anomaly Detection Approach in Cyber Security for User and Entity Behavior Analytics System V. Muliukha, A. Lukashin, L. Utkin, M. Popov, A. Meldo ...................................p. 251 Quantum Machine Learning Quantum Machine Learning J. D. Martín-Guerrero, L. Lamata.......................................................................p. 257 Machine learning framework for control in classical and quantum domains A. Dalal, E. J. Páez, S. S. Vedaie, B. C. Sanders.................................................p. 267 Understanding and improving unsupervised training of Boltzman machines P. Grzybowski, G. Muñoz-Gil, A. Pozas-Kerstjens, M. A. Garcia-March, M. Lewenstein......................................................................................................p. 273 Quantum-Inspired Learning Vector Quantization for Classification Learning T. Villmann, J. Ravichandran, A. Engelsberger, A. Villmann, M. Kaden............p. 279 An quantum algorithm for feedforward neural networks tested on existing quantum hardware D. Bajoni, D. Gerace, C. Macchiavello, F. Tacchino, P. Barkoutsos, I. Tavernelli ......................................................................................................... p. 285 Approximating Archetypal Analysis Using Quantum Annealing S. Feld, C. Roch, K. Geirhos, T. Gabor ............................................................... p. 291 Explorations in Quantum Neural Networks with Intermediate Measurements L. Franken, B. Georgiev ...................................................................................... p. 297 Recurrent networks and reinforcement learning A Distributed Neural Network Architecture for Robust Non-LinearSpatio-Temporal Prediction M. Karlbauer, S. Otte, H. Lensch, T. Scholten, V. Wulfmeyer, M. Butz...............p. 303 Softmax Recurrent Unit: A new type of RNN cell L. Vos, T. van Laarhoven.....................................................................................p. 309 Language Grounded Task-Adaptation in Reinforcement Learning M. Hutsebaut-Buysse, K. Mets, S. Latré .............................................................. p. 315 Object-centered Fourier Motion Estimation and Segment-Transformation Prediction M. Wolter, A. Yao, S. Behnke...............................................................................p. 321 Recurrent Feedback Improves Recognition of Partially Occluded Objects M. R. Ernst, J. Triesch, T. Burwick......................................................................p. 327 Sequence Classification using Ensembles of Recurrent Generative Expert Modules M. Hobbhahn, M. Butz, S. Fabi, S. Otte .............................................................. p. 333 Epistemic Risk-Sensitive Reinforcement Learning H. Eriksson, C. Dimitrakakis...............................................................................p. 339 Tournament Selection Improves Cartesian Genetic Programming for Atari Games T. Cofala, L. Elend, O. Kramer ...........................................................................p. 345 Handling missing data in recurrent neural networks for air quality forecasting M. Tokic, A. von Beuningen, C. Tietz, H.-G. Zimmermann .................................p. 351 Unsupervised learning Self-organizing maps in manifolds with complex topologies: An application to the planning of closed path for indoor UAV patrols H. Frezza-Buet.....................................................................................................p. 357 Detection of abnormal driving situations using distributed representations and unsupervised learning F. Mirus, T. C. Stewart, J. Conradt .....................................................................p. 363 Comparison of Cluster Validity Indices and Decision Rules for Different Degrees of Cluster Separation S. Kaczynska, R. Marion, R. von Sachs ............................................................... p. 369 Feature selection and dimensionality reduction Sparse Metric Learning in Prototype-based Classification J. Brinkrolf, B. Hammer ......................................................................................p. 375 Joint optimization of predictive performance and selection stability V. Hamer, P. Dupont ...........................................................................................p. 381 Perplexity-free Parametric t-SNE F. Crecchi, C. de Bodt, M. Verleysen, J. Lee, D. Bacciu.....................................p. 387 Explaining t-SNE Embeddings Locally by Adapting LIME A. Bibal, V . M. VU, G. Nanfack, B. Frénay ......................................................... p. 393 Do we need hundreds of classifiers or a good feature selection? L. Morán-Fernández, V. Bolón-Canedo, A. Alonso-Betanzos.............................p. 399 Random Projection in supervised non-stationary environments M. Heusinger, F.-M. Schleif ................................................................................p. 405 On Feature Selection Using Anisotropic General Regression Neural Network F. Amato, F. Guignard, P. Jacquet, M. Kanevski................................................p. 411 Statistical learning and optimization A preconditioned accelerated stochastic gradient descent algorithm A. Onose, S. I. Mossavat, H.-J. H. Smilde ........................................................... p. 417 Improving the Union Bound: a Distribution Dependent Approach L. Oneto, S. Ridella, D. Anguita .......................................................................... p. 423 Compressive Learning of Generative Networks V. Schellekens, L. Jacques...................................................................................p. 429 Learning Step Size Adaptation in Evolution Strategies O. Kramer ............................................................................................................ p. 435 Tensor Decompositions in Deep Learning Tensor Decompositions in Deep Learning D. Bacciu, D. Mandic .......................................................................................... p. 441 Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data D. Castellana, D. Bacciu ..................................................................................... p. 451 Mining Temporal Changes in Strengths and Weaknesses of Cricket PlayersUsing Tensor Decomposition S. R. Behera, V. Saradhi......................................................................................p. 457 Image and text analysis 3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution Y. Zhao, N. Wandel, M. Landl, A. Schnepf, S. Behnke.........................................p. 463 Respiratory Pattern Recognition from Low-Resolution Thermal Imaging S. Aario, A. Gorad, M. Arvonen, S. Sarkka..........................................................p. 469 Missing Image Data Imputation using Variational Autoencoders withWeighted Loss R. Cardoso Pereira, J. Santos, J. Pereira Amorim, P. Pereira Rodrigues, P. Henriques Abreu .............................................................................................p. 475 Seq-to-NSeq model for multi-summary generation G. Le Berre, C. Cerisara .....................................................................................p. 481 CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning J. Ferreira, A. Junior, Y. M. Galvao, B. Fernandes, P. Barros...........................p. 487 Learning from partially labeled data Learning from partially labeled data S. Mehrkanoon, X. Huang, J. Suykens.................................................................p. 493 Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks B. Pérez Orozco, S. J. Roberts.............................................................................p. 503 Domain Invariant Representations with Deep Spectral Alignment C. Raab, P. Meier, F.-M. Schleif .........................................................................p. 509 Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling R. Vogel, M. Achab, S. Clémençon, C. Tillier......................................................p. 515 Modelling human sound localization with deep neural networks. K. van der Heijden, S. Mehrkanoon.....................................................................p. 521 A Real-time PCB Defect Detector Based on Supervised and Semi-supervised Learning F. He, S. Tang, S. Mehrkanoon, X. Huang, J. Yang.............................................p. 527 Machine learning in the pharmaceutical industry Machine learning in the biopharma industry G. de Lannoy, T. Helleputte, P. Smyth.................................................................p. 533 Deep Learning to Detect Bacterial Colonies for the Production of Vaccines P. Smyth, J. Lee, G. de Lannoy, T. Beznik ...........................................................p. 541 A Systematic Assessment of Deep Learning Models for Molecule Generation D. Rigoni, N. Navarin, A. Sperduti ...................................................................... p. 547 An agile machine learning project in pharma - developing a Mask R-CNN-based web application for bacterial colony counting P. Smyth, T. Naets, G. de Lannoy, L. Sorber .......................................................p. 553 Frontiers in Reservoir Computing Frontiers in Reservoir Computing C. Gallicchio, M. Lukoševičius, S. Scardapane...................................................p. 559 Reservoir memory machines B. Paassen, A. Schulz...........................................................................................p. 567 Pyramidal Graph Echo State Networks F. M. Bianchi, C. Gallicchio, A. Micheli.............................................................p. 573 Simplifying Deep Reservoir Architectures C. Gallicchio, A. Micheli, A. Sisbarra ................................................................. p. 579 Self-organized dynamic attractors in recurrent neural networks B. Vettelschoss, M. Freiberger, J. Dambre..........................................................p. 585 Self-Organizing Kernel-based Convolutional Echo State Network for Human Actions Recognition G. C. Lee, C. K. Loo, W. S. Liew, S. Wermter......................................................p. 591 Language processing in the era of deep learning Language processing in the era of deep learning I. Lauriola, A. Lavelli, F . Aiolli ........................................................................... p. 597 Modular Length Control for Sentence Generation K. Kudashkina, P. Wittek, J. Kiros, G. W. Taylor................................................p. 607 Entity-Pair Embeddings for Improving Relation Extraction in the Biomedical Domain F. Mehryary, H. Moen, T. Salakoski, F. Ginter...................................................p. 613 Adversarials-1 in Speech Recognition: Detection and Defence N. Worzyk, S. Niewerth, O. Kramer.....................................................................p. 619 On the long-term learning ability of LSTM LMs W. Boes, R. Van Rompaey, L. Verwimp, J. Pelemans, H. Van hamme, P. Wambacq.........................................................................................................p. 625 Cross-Encoded Meta Embedding towards Transfer Learning G. Kovács, R. Brännvall, J. Öhman, M. Liwicki..................................................p. 631 Exploring the feature space of character-level embeddings I. Lauriola, S. Campese, A. Lavelli, F. Rinaldi, F. Aiolli.....................................p. 637 Supervised learning Detection of elementary particles with the WiSARD n-tuple classifier P. Xavier, M. De Gregorio, F. França, P. Lima..................................................p. 643 Automatic Pain Intensity Recognition: Training Set Selection based on Outliers and Centroids P. Bellmann, P. Thiam, F. Schwenker .................................................................p. 649 Binary and Multi-label Defect Classification of Printed Circuit Board basedon Transfer Learning G. Azevedo, L. Silva, A. Junior, B. Fernandes, S. Oliveira..................................p. 655 SDOstream: Low-Density Models for Streaming Outlier Detection A. Hartl, F. Iglesias, T. Zseby..............................................................................p. 661 Locally Adaptive Nearest Neighbors J. P. Göpfert, H. Wersing, B. Hammer ................................................................p. 667 Equilibrium Propagation for Complete Directed Neural Networks M. Tristany Farinha, S. Pequito, P. A. Santos, M. Figueiredo............................p. 673 On-edge adaptive acoustic models: an application to acoustic person presence detection L. Vuegen, P. Karsmakers ...................................................................................p. 679 Gaussian process regression for the estimation of stable univariate time-series processes G. Birpoutsoukis, J. M. Hendrickx.......................................................................p. 685 Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression J. Hämäläinen, T. Kärkkäinen.............................................................................p. 691 Adapting Random Forests to Cope with Heavily Censored Datasets in Survival Analysis T. Pomsuwan, A. Freitas......................................................................................p. 697 Model Variance for Extreme Learning Machine F. Guignard, M. Laib, M. Kanevski.....................................................................p. 703 Multi-Directional Laplacian Pyramids for Completion of Missing Data Entries N. Rabin...............................................................................................................p. 709 Navigational Freespace Detection for Autonomous Driving in Fixed Routes A. Narayan, E. Tuci, W. Sachiti, A. Parsons........................................................p. 715 Similarities between policy gradient methods in reinforcement and supervised learning E. Benhamou, D. Saltiel.......................................................................................p. 721 Author index ................................................................................................ p. 727 Committees ................................................................................................... p. 731 06 03 01 https://i6doc.com/resources/titles/28001100609140/images/eeb358fcbf46e89566ad3fe0707820a0/HIGHQ/9782875870742.jpg 20201021 07 03 01 https://i6doc.com/resources/titles/28001100609140/images/eeb358fcbf46e89566ad3fe0707820a0/THUMBNAIL/9782875870742.jpg 20201021 38 https://i6doc.com/en/book/?GCOI=28001100609140 ESANN 01 ESANN Louvain-la-Neuve BE 04 20201021 621 2020 02 WORLD