ESANN 2020 - Proceedings

28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (online event)
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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.


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Publisher
ESANN
Contributions by
,
Language
English
BISAC Subject Heading
TEC000000 TECHNOLOGY & ENGINEERING
BIC subject category (UK)
T Technology, engineering, agriculture
Onix Audience Codes
06 Professional and scholarly
Title First Published
21 October 2020
Type of Work
Proceedings
Includes
Index

Livre broché


Publication Date
2009
Extent
Main content page count : 203
Code
EBHPAS
Weight
303 grams
List Price
25.00 €
ONIX XML
Version 2.1, Version 3

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Contents


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 in electrocorticography
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-Linear Spatio-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 Players Using 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 with Weighted 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 based on 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