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ORCID27th ESANN 2019: Bruges, Belgium
- 27th European Symposium on Artificial Neural Networks, ESANN 2019, Bruges, Belgium, April 24-26, 2019. 2019
Classification and Bayesian learning
- Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre:
Conditional BRUNO: a neural process for exchangeable labelled data. - Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
interpretable dynamics models for data-efficient reinforcement learning. - Luca Oneto, Michele Donini, Massimiliano Pontil:
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. - Jensun Ravichandran, Sascha Saralajew, Thomas Villmann:
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization. - Cyprien Ruffino, Romain Hérault, Eric Laloy, Gilles Gasso:
Pixel-wise Conditioning of Generative Adversarial Networks. - Roberto Alamino:
Committees as Artificial Organisms - Evolution and Adaptation. - Maximilian Münch, Karsten Huffstadt, Frank-Michael Schleif
:
Towards a device-free passive presence detection system with Bluetooth Low Energy beacons. - Greg Collinge, Emil C. Lupu, Luis Muñoz-González:
Defending against poisoning attacks in online learning settings. - Jarno Kansanaho, Tommi Kärkkäinen:
Hybrid vibration signal monitoring approach for rolling element bearings. - Bo Li, Mathieu Dehouck, Pascal Denis:
Modal sense classification with task-specific context embeddings. - István Megyeri, István Hegedüs, Márk Jelasity:
Adversarial robustness of linear models: regularization and dimensionality. - Sujay Khandagale, Rohit Babbar:
A Simple and Effective Scheme for Data Pre-processing in Extreme Classification. - Ángel Campo, Marc Francaux, Laurent Baijot, Michel Verleysen:
MAP best performances prediction for endurance runners. - Gaëlle Loosli:
TrIK-SVM : an alternative decomposition for kernel methods in Kreı̆n spaces.
Embeddings and Representation Learning for Structured Data
- Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Alessandro Sperduti:
Embeddings and Representation Learning for Structured Data. - Davide Bacciu, Alessio Micheli, Marco Podda:
Graph generation by sequential edge prediction. - Nicolò Navarin, Dinh Van Tran, Alessandro Sperduti:
On the definition of complex structured feature spaces. - Nils M. Kriege:
Deep Weisfeiler-Lehman assignment kernels via multiple kernel learning. - Florian Mirus, Peter Blouw, Terrence C. Stewart, Jörg Conradt:
Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions. - Jelle Bakker, Kerstin Bunte:
Efficient learning of email similarities for customer support. - Cécile Hautecoeur, François Glineur:
Nonnegative matrix factorization with polynomial signals via hierarchical alternating least squares.
Deep learning and CNN
- Florent Forest, Mustapha Lebbah, Hanene Azzag, Jérôme Lacaille:
Deep Embedded SOM: joint representation learning and self-organization. - Enrico Grisan, Alessandro Zandonà, Barbara Di Camillo:
Deep convolutional neural network for survival estimation of Amyotrophic Lateral Sclerosis patients. - Jonathan Peck, Bart Goossens, Yvan Saeys:
Detecting adversarial examples with inductive Venn-ABERS predictors. - Onkar Arun Pandit, Pascal Denis, Liva Ralaivola:
Learning Rich Event Representations and Interactions for Temporal Relation Classification. - Ismaïla Seck, Gaëlle Loosli, Stéphane Canu:
L1-norm double backpropagation adversarial defense. - Ana M. Barragan-Montero, Dan Nguyen, Weiguo Lu, Mu-Han Lin, Xavier Geets, Edmond Sterpin, Steve B. Jiang:
Application of deep neural networks for automatic planning in radiation oncology treatments. - Florian Patzelt, Robert Haschke, Helge J. Ritter:
Conditional WGAN for grasp generation. - Liriam Enamoto, Weigang Li:
Multilingual short text categorization using convolutional neural network. - Lukas Hahn, Lutz Roese-Koerner, Klaus Friedrichs, Anton Kummert:
Fast and reliable architecture selection for convolutional neural networks. - Anas Albaghajati, Lahouari Ghouti:
On the Speedup of Deep Reinforcement Learning Deep Q-Networks (RL-DQNs). - Krishna Mohan Mishra, Tomi Krogerus, Kalevi Huhtala:
Deep Autoencoder Feature Extraction for Fault Detection of Elevator Systems. - Magnus Stavngaard, August Sørensen, Stephan Lorenzen, Niklas Hjuler, Stephen Alstrup:
Detecting Ghostwriters in High Schools. - Leonardo Tavares Oliveira, Min Soo Kim, Alberto A. Del Barrio García, Nader Bagherzadeh, Ricardo Menotti:
Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier. - Danut Ovidiu Pop, Alexandrina Rogozan, Fawzi Nashashibi, Abdelaziz Bensrhair:
Improving Pedestrian Recognition using Incremental Cross Modality Deep Learning. - Paul Smyth, Gaël de Lannoy, Moritz von Stosch, Alexander Pysik, Amin Khan:
Machine learning in research and development of new vaccines products: opportunities and challenges. - Matias Valdenegro-Toro, Octavio Arriaga, Paul Plöger:
Real-time Convolutional Neural Networks for emotion and gender classification.
Learning methods and optimization
- Simon Carbonnelle, Christophe De Vleeschouwer:
Experimental study of the neuron-level mechanisms emerging from backpropagation. - Lukas Enderich, Fabian Timm, Lars Rosenbaum, Wolfram Burgard:
Learning multimodal fixed-point weights using gradient descent. - Shuyu Dong, Pierre-Antoine Absil, Kyle A. Gallivan:
Preconditioned conjugate gradient algorithms for graph regularized matrix completion. - Matthias Treder:
Direct calculation of out-of-sample predictions in multi-class kernel FDA. - Niloofar Azizi, Nils Wandel, Sven Behnke:
Complex Valued Gated Auto-encoder for Video Frame Prediction. - Joseph Rynkiewicz:
On overfitting of multilayer perceptrons for classification. - Luca Masera, Enrico Blanzieri:
Very Simple Classifier: a concept binary classifier to investigate features based on subsampling and locality. - Madson Luiz Dantas Dias, Lucas Silva de Sousa, Ajalmar R. da Rocha Neto, César L. C. Mattos, João P. P. Gomes, Tommi Kärkkäinen:
Sparse minimal learning machine using a diversity measure minimization. - Emilie Renard, Pierre-Antoine Absil, Kyle A. Gallivan:
Minimax center to extract a common subspace from multiple datasets. - Estelle M. Massart, Pierre-Yves Gousenbourger, Nguyen Thanh Son, Tatjana Stykel, Pierre-Antoine Absil:
Interpolation on the manifold of fixed-rank positive-semidefinite matrices for parametric model order reduction: preliminary results. - Stefan Mautner, Rolf Backofen, Fabrizio Costa:
Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings.
60 Years of Weightless Neural Systems
- Igor Aleksander, Helen Morton:
Systems with 'subjective feelings' - the perspective from weightless automata. - Leopoldo Lusquino Filho, Luiz F. R. Oliveira, Aluizio S. Lima Filho, Gabriel P. Guarisa, Priscila Machado Vieira Lima, Felipe Maia Galvão França:
Prediction of palm oil production with an enhanced n-Tuple Regression Network. - Leandro Santiago de Araújo, Letícia Dias Verona, Fábio Medeiros Rangel, Fabrício Firmino de Faria, Daniel Sadoc Menasché, Wouter Caarls, Maurício Breternitz, Sandip Kundu, Priscila Machado Vieira Lima, Felipe Maia Galvão França:
Memory Efficient Weightless Neural Network using Bloom Filter. - Priscila G. M. dos Santos, Rodrigo S. Sousa, Adenilton J. da Silva:
A WNN model based on Probabilistic Quantum Memories. - Eduardo S. Ribeiro, Vitor A. M. F. Torres, Brayan James, Mateus T. Braga, Elcio H. Shiguemori, Haroldo F. de Campos Velho, Luiz C. B. Torres, Antônio P. Braga:
Weightless neural systems for deforestation surveillance and image-based navigation of UAVs in the Amazon forest. - Maurizio Giordano, Massimo De Gregorio:
An evolutionary approach for optimizing weightless neural networks. - Luis Filipe Kopp, José Barbosa-Filho, Priscila Machado Vieira Lima, Claudio M. de Farias:
Modeling Sparse Data as Input for Weightless Neural Network.
Domain adaptation and learning
- Konstantinos Sechidis, Eleftherios Spyromitros Xioufis, Ioannis P. Vlahavas:
Multi-target feature selection through output space clustering. - Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tiño, Barbara Hammer:
Feature relevance bounds for ordinal regression. - Viet Minh Vu, Benoît Frénay:
User-steering interpretable visualization with probabilistic principal components analysis. - Jiajun Pan, Hoel Le Capitaine:
Metric learning with submodular functions. - Arijit Ukil, Pankaj Malhotra, Soma Bandyopadhyay, Tulika Bose, Ishan Sahu, Ayan Mukherjee, Lovekesh Vig, Arpan Pal, Gautam Shroff:
Fusing Features based on Signal Properties and TimeNet for Time Series Classification. - Jiajun Pan, Hoel Le Capitaine:
Metric learning with relational data. - Jussi Rasku, Nysret Musliu, Tommi Kärkkäinen:
Feature and Algorithm Selection for Capacitated Vehicle Routing Problems. - Siwen Guo, Sviatlana Höhn, Christoph Schommer:
Topic-based historical information selection for personalized sentiment analysis. - Christos Athanasiadis, Enrique Hortal, Stylianos Asteriadis:
Bridging face and sound modalities through domain adaptation metric learning. - Tommi Kärkkäinen:
Model selection for Extreme Minimal Learning Machine using sampling. - Michael C. Thrun:
Knowledge Discovery in Quarterly Financial Data of Stocks Based on the Prime Standard using a Hybrid of a Swarm with SOM. - Travis Wiens:
Dimensionality reduction in a hydraulic valve positioning application. - Cyril de Bodt, Dounia Mulders, Daniel López Sánchez, Michel Verleysen, John A. Lee:
Class-aware t-SNE: cat-SNE. - Najmeh Abiri, Mattias Ohlsson:
Variational auto-encoders with Student's t-prior.
Streaming data analysis, concept drift and analysis of dynamic data sets
- Albert Bifet, Barbara Hammer, Frank-Michael Schleif:
Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. - Waqas Jamil, Abdelhamid Bouchachia:
Online Bayesian Shrinkage Regression. - Christoph Raab, Moritz Heusinger, Frank-Michael Schleif:
Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. - Lukas Fleckenstein, Sebastian Kauschke, Johannes Fürnkranz:
Beta Distribution Drift Detection for Adaptive Classifiers. - Pekka Siirtola, Heli Koskimäki, Juha Röning:
Importance of user inputs while using incremental learning to personalize human activity recognition models.
Societal Issues in Machine Learning: When Learning from Data is Not Enough
- Davide Bacciu, Battista Biggio, Paulo Lisboa, José D. Martín, Luca Oneto, Alfredo Vellido:
Societal Issues in Machine Learning: When Learning from Data is Not Enough. - Andrew Yale, Saloni Dash, Ritik Dutta, Isabelle Guyon, Adrien Pavao, Kristin P. Bennett:
Privacy Preserving Synthetic Health Data. - Charlotte Ducuing, Luca Oneto, Renzo Canepa:
Fairness and Accountability of Machine Learning Models in Railway Market: are Applicable Railway Laws Up to Regulate Them? - Benjamin Paaßen, Astrid Bunge, Carolin Hainke, Leon Sindelar, Matthias Vogelsang:
Dynamic fairness - Breaking vicious cycles in automatic decision making. - Francesco Crecchi, Davide Bacciu, Battista Biggio:
Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction. - Olov Andersson, Patrick Doherty:
Deep RL for autonomous robots: limitations and safety challenges. - Andrea Apicella, Francesco Isgrò, Roberto Prevete, Andrea Sorrentino, Guglielmo Tamburrini:
Explaining classification systems using sparse dictionaries.
Statistical physics of learning and inference
- Michael Biehl, Nestor Caticha, Manfred Opper, Thomas Villmann:
Statistical physics of learning and inference. - Nestor Caticha, Felippe Alves:
Trust, law and ideology in a NN agent model of the US Appellate Courts. - Michiel Straat, Michael Biehl:
On-line learning dynamics of ReLU neural networks using statistical physics techniques. - Ying Fang, Zhaofei Yu, Feng Chen:
Noise helps optimization escape from saddle points in the neural dynamics.
Image processing and transfer learning
- Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten, Ann Nowé:
Deep hybrid approach for 3D plane segmentation. - Florian Franzen, Chunrong Yuan:
visualizing image classification in fourier domain. - Shaon Sutradhar, José Rouco, Marcos Ortega:
Blind-spot network for image anomaly detection: A new approach to diabetic retinopathy screening. - Lorand Dobai, Mihai Teletin:
A document detection technique using convolutional neural networks for optical character recognition systems. - Ali Oguz Uzman, Jannis Horn, Sven Behnke:
Learning super-resolution 3D segmentation of plant root MRI images from few examples. - Julien Randon-Furling, William Clark, Madalina Olteanu:
Analyzing spatial dissimilarities in high-resolution geo-data : a case study of four European cities. - Iago Otero Coto, Plácido Francisco Lizancos Vidal, Joaquim de Moura, Jorge Novo, Marcos Ortega:
Computerized tool for identification and enhanced visualization of Macular Edema regions using OCT scans. - Hygor Xavier Araújo, Raul Fonseca Neto, Saulo Moraes Villela:
A best-first branch-and-bound search for solving the transductive inference problem using support vector machines. - Benjamin Donnot, Balthazar Donon, Isabelle Guyon, Zhengying Liu, Antoine Marot, Patrick Panciatici, Marc Schoenauer:
LEAP nets for power grid perturbations. - Rinu Boney, Alexander Ilin:
Active one-shot learning with Prototypical Networks. - Patrick Menz, Andreas Backhaus, Udo Seiffert:
Transfer Learning for transferring machine-learning based models among hyperspectral sensors.
Time series and signal processing
- Babak Hosseini, Barbara Hammer:
Multiple-Kernel dictionary learning for reconstruction and clustering of unseen multivariate time-series. - Dounia Mulders, Cyril de Bodt, Nicolas Lejeune, John A. Lee, André Mouraux, Michel Verleysen:
Tensor factorization to extract patterns in multimodal EEG data. - Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Beyond Pham's algorithm for joint diagonalization. - Hafez Farazi, Sven Behnke:
Frequency Domain Transformer Networks for Video Prediction. - Claudio Gallicchio, Alessio Micheli, Luca Pedrelli:
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction. - Matteo Maggiolo, Gerasimos Spanakis:
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction. - Rafael Thomazi Gonzalez, Dante Augusto Couto Barone:
Using Deep Learning and Evolutionary Algorithms for Time Series Forecasting. - Luca Cerina, Giuseppe Franco, Marco Domenico Santambrogio:
lightweight autonomous bayesian optimization of Echo-State Networks. - Manxing Du, Christian A. Hammerschmidt, Georgios Varisteas, Radu State, Mats Brorsson, Zhu Zhang:
time series modelling of market price in real-time bidding.
Dynamical systems and reinforcement learning
- Florian Mirus, Benjamin Zorn, Jörg Conradt:
Short-term trajectory planning using reinforcement learning within a neuromorphic control architecture. - Viktor Schmuck, David Meredith:
training networks separately on static and dynamic obstacles improves collision avoidance during indoor robot navigation. - Cristian Millán, Bruno J. T. Fernandes, Francisco Cruz:
Human feedback in continuous actor-critic reinforcement learning. - Claudio Gallicchio:
Chasing the Echo State Property.
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