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26th ESANN 2018: Bruges, Belgium
- 26th European Symposium on Artificial Neural Networks, ESANN 2018, Bruges, Belgium, April 25-27, 2018. 2018
Deep learning and image processing
- Henrique Siqueira, Pablo V. A. Barros, Sven Magg, Cornelius Weber, Stefan Wermter:
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition. - Miguel Angrick, Christian Herff, Garett D. Johnson, Jerry J. Shih, Dean J. Krusienski, Tanja Schultz:
interpretation of convolutional neural networks for speech regression from electrocorticography. - Qi Wang, Mickaël Chen, Thierry Artières, Ludovic Denoyer:
transferring style in motion capture sequences with adversarial learning. - Nils Worzyk, Oliver Kramer:
Properties of adv-1 - Adversarials of Adversarials. - Manfred Eppe, Tayfun Alpay, Fares Abawi, Stefan Wermter:
An analysis of subtask-dependency in robot command interpretation with dilated CNNs. - Aymen Cherif, Salim Jouili:
Image retrieval and ranking through Deep Comparative Neural Networks. - Alexander Gepperth, Saad Abdullah Gondal:
Incremental learning with deep neural networks using a test-time oracle. - Sebastian Springenberg, Egor Lakomkin, Cornelius Weber, Stefan Wermter:
Image-to-Text Transduction with Spatial Self-Attention. - Jörg Wagner, Volker Fischer, Michael Herman, Sven Behnke:
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets. - Florian Mirus, Terrence C. Stewart, Jörg Conradt:
Towards cognitive automotive environment modelling: reasoning based on vector representations. - Tobias Hinz, Stefan Wermter:
Inferencing based on unsupervised learning of disentangled representations. - Ion Alexandru Marinescu, Zoltán Bálint, Laura Diosan, Anca Andreica:
Dynamic autonomous image segmentation based on Grow Cut. - Peer Springstübe, Stefan Heinrich, Stefan Wermter:
Continuous convolutional object tracking. - Daniela Onita, Adriana Birlutiu:
Active Learning based on Transfer Learning Techniques for Image Classification. - Leopoldo Lusquino Filho, Felipe M. G. França, Priscila M. V. Lima:
Near-optimal facial emotion classification using a WiSARD-based weightless system. - Murat Kirtay, Lorenzo Vannucci, Ugo Albanese, Alessandro Ambrosano, Egidio Falotico, Cecilia Laschi:
Spatial pooling as feature selection method for object recognition.
Interaction and User Integration in Machine Learning for Information Visualisation
- Benoît Frénay, Bruno Dumas, John A. Lee:
Information visualisation and machine learning: latest trends towards convergence. - René Cutura, Stefan Holzer, Michaël Aupetit, Michael Sedlmair:
VisCoDeR: A tool for visually comparing dimensionality reduction algorithms. - Udo Schlegel, Eren Cakmak, Juri Buchmüller, Daniel A. Keim:
G-Rap: interactive text synthesis using recurrent neural network suggestions. - Ignacio Díaz Blanco, Daniel Pérez, Abel Alberto Cuadrado Vega, Diego García-Pérez, Manuel Domínguez-González:
Interactive dimensionality reduction of large datasets using interpolation.
Nonlinear dimensionality reduction
- Cyril de Bodt, Dounia Mulders, Michel Verleysen, John A. Lee:
Perplexity-free t-SNE and twice Student tt-SNE. - Joachim Schreurs, Johan A. K. Suykens:
Generative Kernel PCA. - Cyril de Bodt, Dounia Mulders, Michel Verleysen, John A. Lee:
Extensive assessment of Barnes-Hut t-SNE. - Tiago Santos, Roman Kern:
Understanding wafer patterns in semiconductor production with variational auto-encoders.
Classification
- Laura Isabel Galindez Olascoaga, Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Feature noise tuning for resource efficient Bayesian Network Classifiers. - Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann:
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. - Mazen Salous, Felix Putze:
behaviour-based working memory capacity classification using recurrent neural networks. - Victor Amaral de Sousa, Anthony Simonofski, Monique Snoeck, Ivan Jureta:
Structuring and Solving Multi-Criteria Decision Making Problems using Artificial Neural Networks: a smartphone recommendation case. - Christian Limberg, Heiko Wersing, Helge J. Ritter:
Efficient accuracy estimation for instance-based incremental active learning. - Mirko Polato, Fabio Aiolli:
Boolean kernels for interpretable kernel machines. - Ivano Lauriola, Mirko Polato, Fabio Aiolli:
The minimum effort maximum output principle applied to Multiple Kernel Learning. - Katharina Hofer-Schmitz, Phuong-Ha Nguyen, Kristian Berwanger:
One-class Autoencoder approach to classify Raman spectra outliers. - Joao Victor Bruneti Severino, Alessandro Zimmer, Leandro dos Santos Coelho, Roberto Zanetti Freire:
Radar Based Pedestrian Detection using Support Vector Machine and the Micro Doppler Effect. - Madson Luiz Dantas Dias, Lucas Silva de Sousa, Ajalmar R. da Rocha Neto, Amauri H. Souza Júnior:
Opposite neighborhood: a new method to select reference points of minimal learning machines. - David Twomey, Denise Gorse:
A neural network cost function for highly class-imbalanced data sets. - Ralf Schönherr, Maximilian Knaller, Markus Philipp:
Self-learning assembly systems during ramp-up. - Babak Hosseini, Barbara Hammer:
Feasibility based Large Margin Nearest Neighbor metric learning. - Christine Sinoquet, Kamel Mekhnacha:
Combining latent tree modeling with a random forest-based approach, for genetic association studies. - Francesco Calimeri
, Aldo Marzullo, Claudio Stamile, Giorgio Terracina:
Graph based neural networks for automatic classification of multiple sclerosis clinical courses.
Regression and recommendation systems
- Tommi Kärkkäinen:
Extreme Minimal Learning Machine. - Moussab Djerrab, Alexandre Garcia:
Learning with a Fisher surrogate loss in a small data regime. - Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick Panciatici:
Fast Power system security analysis with Guided Dropout. - Josef Feigl, Martin Bogdan:
Neural Networks for Implicit Feedback Datasets. - Charles-Emmanuel Dias, Vincent Guigue, Patrick Gallinari:
Regularize and explicit collaborative filtering with textual attention. - Heitor Murilo Gomes, Jean Paul Barddal, Luis Eduardo Boiko Ferreira, Albert Bifet:
Adaptive random forests for data stream regression. - Imen Chakroun, Tom Vander Aa, Thomas J. Ashby:
Cache-efficient Gradient Descent Algorithm. - Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft, Thomas A. Runkler:
Sensitivity analysis for predictive uncertainty. - Alejandro Catalina, Carlos M. Alaíz, José R. Dorronsoro:
Revisiting FISTA for Lasso: Acceleration Strategies Over The Regularization Path.
Shallow and Deep models for transfer learning and domain adaptation
- Siamak Mehrkanoon, Matthew B. Blaschko, Johan A. K. Suykens:
Shallow and Deep Models for Domain Adaptation problems. - Debjeet Majumdar, Vinay P. Namboodiri:
Unsupervised domain adaptation of deep object detectors.
Machine Learning and Data Analysis in Astroinformatics
- Michael Biehl, Kerstin Bunte, Giuseppe Longo, Peter Tiño:
Machine learning and data analysis in astroinformatics. - Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach:
Anomaly detection in star light curves using hierarchical Gaussian processes. - Pablo Huijse, Nicolas Astorga, Pablo A. Estévez, Giuliano Pignata:
Latent representations of transient candidates from an astronomical image difference pipeline using Variational Autoencoders. - Mohammad Mohammadi, Reynier Peletier, Frank-Michael Schleif
Globular Cluster Detection in the Gaia Survey. - Michele Delli Veneri, Stefano Cavuoti, Massimo Brescia, Giuseppe Riccio, Giuseppe Longo:
stellar formation rates in galaxies using machine learning models. - Aleke Nolte, Lingyu Wang, Michael Biehl:
Prototype-based analysis of GAMA galaxy catalogue data.
Deep Learning in Bioinformatics and Medicine
- Davide Bacciu, Paulo Lisboa, José D. Martín, Ruxandra Stoean, Alfredo Vellido:
Bioinformatics and medicine in the era of deep learning. - Jan Wülfing, Sreedhar S. Kumar, Joschka Boedecker, Martin A. Riedmiller, Ulrich Egert:
Controlling biological neural networks with deep reinforcement learning. - Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. - Isaac Fernández-Varela, Dimitrios Athanasakis, Samuel Parsons, Elena Hernández-Pereira, Vicente Moret-Bonillo:
Sleep staging with deep learning: a convolutional model. - José Pereira Amorim, Inês Domingues, Pedro Henriques Abreu, João A. M. Santos:
Interpreting deep learning models for ordinal problems. - Miguel A. Atencia Ruiz, Ruxandra Stoean:
Non-negative Matrix Factorization for Medical Imaging. - Ioana Bica, Petar Velickovic, Hui Xiao:
Multi-omics data integration using cross-modal neural networks. - Dinh Tran-Van, Nicolò Navarin, Alessandro Sperduti:
DEEP: decomposition feature enhancement procedure for graphs. - Claudio Gallicchio, Alessio Micheli, Luca Pedrelli:
Deep Echo State Networks for Diagnosis of Parkinson's Disease. - Umair Javaid, John A. Lee:
Capturing variabilities from Computed Tomography images with Generative Adversarial Networks (GANs). - Natalia Khanzhina, Evgeny Putin, Andrey Filchenkov, Elena Zamyatina:
Pollen grain recognition using convolutional neural network.
Randomized Neural Networks
- Claudio Gallicchio, Alessio Micheli, Peter Tiño:
Randomized Recurrent Neural Networks. - Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Bidirectional deep-readout echo state networks. - Ali Rodan, Pedro A. Castillo, Hossam Faris, Antonio Miguel Mora, Huthaifa Jawazneh
Forecasting Business Failure in Highly Imbalanced Distribution based on Delay Line Reservoir. - Hikmat Dashdamirov, Sebastián Basterrech:
Estimation of the Human Concentration using Echo State Networks. - Tomás Buriánek, Sebastián Basterrech:
Quantifying the Reservoir Quality using Dimensionality Reduction Techniques.
Clustering and feature selection
- Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi:
Scalable robust clustering method for large and sparse data. - Lauriane Castin, Benoît Frénay:
clustering with decision trees: divisive and agglomerative approach. - Marko Niemelä, Sami Äyrämö, Tommi Kärkkäinen:
Comparison of cluster validation indices with missing data. - Raúl Santos-Rodríguez, Niall Twomey:
Efficient approximate representations for computationally expensive features. - Bao Tuyen Huynh, Faicel Chamroukhi:
Regularised maximum-likelihood inference of mixture of experts for regression and clustering. - Noelia Sánchez-Maroño, Beatriz Pérez-Sánchez:
Feature selection for label ranking. - Mohamed Laib, Mikhail F. Kanevski:
A novel filter algorithm for unsupervised feature selection based on a space filling measure.
Mathematical aspects of learning, and reinforcement learning
- Joseph Rynkiewicz:
Asymptotic statistics for multilayer perceptron with ReLu hidden units. - Luca Oneto, Sandro Ridella, Davide Anguita:
Local Rademacher Complexity Machine. - Khadija Musayeva, Fabien Lauer, Yann Guermeur:
A sharper bound on the Rademacher complexity of margin multi-category classifiers. - Muhammad Burhan Hafez, Matthias Kerzel, Cornelius Weber, Stefan Wermter:
Slowness-based neural visuomotor control with an Intrinsically motivated Continuous Actor-Critic. - Guillaume Olikier, Pierre-Antoine Absil, Lieven De Lathauwer:
A variable projection method for block term decomposition of higher-order tensors. - Ye-Sheen Lim, Denise Gorse:
Reinforcement Learning for High-Frequency Market Making.
Emerging trends in machine learning: beyond conventional methods and data
- Luca Oneto, Nicolò Navarin, Michele Donini, Davide Anguita:
Emerging trends in machine learning: beyond conventional methods and data. - Adrien Bibal, Rebecca Marion, Benoît Frénay:
Finding the most interpretable MDS rotation for sparse linear models based on external features. - Davide Bacciu, Daniele Castellana:
Mixture of Hidden Markov Model as Tree Encoder. - Alessio Carrega:
Set point thresholds from topological data analysis and an outlier detector. - Johannes Brinkrolf, Kolja Berger, Barbara Hammer:
Differential private relevance learning. - Stéphan Clémençon, Anna Korba:
On aggregation in ranking median regression. - Daegun Won, Peter J. Jansen, Jaime G. Carbonell:
Temporal transfer learning for drift adaptation. - Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Beatriz Pérez-Sánchez, Marcelo Gómez-Casal:
LANN-DSVD: A privacy-preserving distributed algorithm for machine learning. - Daniel Vieira, Fábio Medeiros Rangel, Fabrício Firmino de Faria, João Paixão:
Vector Field Based Neural Networks.
Temporal data, sequences and incremental learning
- Abraham Traoré, Maxime Berar, Alain Rakotomamonjy:
Non-Negative Tensor Dictionary Learning. - Marcos Antonio Alves, Petrônio Cândido de Lima e Silva, Carlos Alberto Severiano Junior, Gustavo Linhares Vieira, Frederico Gadelha Guimarães, Hossein Javedani Sadaei:
An extension of nonstationary fuzzy sets to heteroskedastic fuzzy time series. - Tayfun Alpay, Fares Abawi, Stefan Wermter:
Surprisal-based activation in recurrent neural networks. - Brieuc Conan-Guez, Alain Gély, Lydia Boudjeloud-Assala, Alexandre Blansché:
K-spectral centroid: extension and optimizations. - Aviv Nahon, Boaz Lerner:
Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm. - Ryan McConville, Raúl Santos-Rodríguez, Niall Twomey:
Person Identification and Discovery With Wrist Worn Accelerometer Data. - Nicolas Khoury, Ferhat Attal, Yacine Amirat, Abdelghani Chibani, Samer Mohammed:
CDTW-based classification for Parkinson's Disease diagnosis. - Pekka Siirtola, Heli Koskimäki, Juha Röning:
Personalizing human activity recognition models using incremental learning. - Claudio Gallicchio:
Short-term Memory of Deep RNN. - Pekka Siirtola, Jukka Komulainen, Vili Kellokumpu:
Effect of context in swipe gesture-based continuous authentication on smartphones.
Impact of Biases in Big Data
- Patrick O. Glauner, Petko Valtchev, Radu State:
Impact of Biases in Big Data. - Borja Seijo-Pardo, Amparo Alonso-Betanzos, Kristin P. Bennett, Verónica Bolón-Canedo, Isabelle Guyon, Julie Josse, Mehreen Saeed:
Analysis of imputation bias for feature selection with missing data. - Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau:
Systematics aware learning : a case study in high energy physics.
Optimization and metaheuristics
- Sarmimala Saikia, Richa Verma, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan:
Evolutionary RL for Container Loading. - Christine Sinoquet, Clément Niel:
Enhancement of a stochastic Markov-blanket framework with ant colony optimization, to uncover epistasis in genetic association studies. - Carlos Eduardo Klein, Leandro dos Santos Coelho:
Meerkats-inspired Algorithm for Global Optimization Problems. - Carlos Eduardo Klein, Viviana Cocco Mariani, Leandro dos Santos Coelho:
Cheetah Based Optimization Algorithm: A Novel Swarm Intelligence Paradigm. - Diogo M. De-Freitas, Plínio de Sá Leitão Júnior, Celso G. Camilo-Junior, Rachel Harrison:
Evolutionary Composition of Customized Fault Localization Heuristics. - Mohamed Salim Amri Sakhri, Mounira Tlili, Hamid Allaoui, Ouajdi Korbaa:
Order Crossover for the Inventory Routing Problem.
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