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Laure Soulier
- ESANN 2021 - Unsupervised Word Representations Learning with Bilinear Convolutional Network on Characters [Details]
- ESANN 2011 - Hybrid HMM and HCRF model for sequence classification [Details]
- ESANN 2009 - Sensors selection for P300 speller brain computer interface [Details]
- ESANN 2021 - Fusion of estimations from two modalities using the Viterbi's algorithm: application to fetal heart rate monitoring [Details]
- ESANN 2016 - Stacked denoising autoencoders for the automatic recognition of microglial cells’ state [Details]
- ESANN 2018 - Opposite neighborhood: a new method to select reference points of minimal learning machines [Details]
- ESANN 2019 - Sparse minimal learning machine using a diversity measure minimization [Details]
- ESANN 2019 - A WNN model based on Probabilistic Quantum Memories [Details]
- ESANN 2017 - Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification [Details]
- ESANN 2010 - Multiple Local Models for System Identification Using Vector Quantization Algorithms [Details]
- ESANN 2012 - texture classification based on symbolic data analysis [Details]
- ESANN 2014 - Credit analysis with a clustering RAM-based neural classifier [Details]
- ESANN 2012 - Recognition of HIV-1 subtypes and antiretroviral drug resistance using weightless neural networks [Details]
- ESANN 2021 - Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions [Details]
- ESANN 2023 - Sun Tracking using a Weightless Q-Learning Neural Network [Details]
- ESANN 2017 - A Robust Minimal Learning Machine based on the M-Estimator [Details]
- ESANN 2018 - Opposite neighborhood: a new method to select reference points of minimal learning machines [Details]
- ESANN 2012 - Relevance learning for time series inspection [Details]
- ESANN 2017 - A performance acceleration algorithm of spectral unmixing via subset selection [Details]
- ESANN 2024 - Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks [Details]
- ESANN 1995 - Cascade learning for FIR-TDNNs [Details]
- ESANN 1996 - Time series prediction using neural networks and its application to artificial human walking [Details]
- ESANN 2008 - Noise influence on correlated activities in a modular neuronal network: from synapses to functional connectivity [Details]
- ESANN 2019 - Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction [Details]
- ESANN 2015 - Comparison of Numerical Models and Statistical Learning for Wind Speed Prediction [Details]
- ESANN 1994 - Improvement of learning results of the selforganizing map by calculating fractal dimensions [Details]
- ESANN 2002 - A general framework for unsupervised processing of structured data [Details]
- ESANN 2004 - a preliminary experimental comparison of recursive neural networks and a tree kernel method for QSAR/QSPR regression tasks [Details]
- ESANN 2005 - Contextual Processing of Graphs using Self-Organizing Maps [Details]
- ESANN 2006 - Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD [Details]
- ESANN 2007 - "Kernelized" Self-Organizing Maps for Structured Data [Details]
- ESANN 2008 - Self-Organizing Maps for cyclic and unbounded graphs [Details]
- ESANN 2009 - Projection of undirected and non-positional graphs using Self Organizing Maps [Details]
- ESANN 2009 - Supervised learning as preference optimization [Details]
- ESANN 2010 - Heuristics Miner for Time Intervals [Details]
- ESANN 2011 - Sparsity Issues in Self-Organizing-Maps for Structures [Details]
- ESANN 2012 - Assessment of sequential Boltmann machines on a lexical processing task [Details]
- ESANN 2012 - Input-Output Hidden Markov Models for trees [Details]
- ESANN 2014 - A HMM-based pre-training approach for sequential data [Details]
- ESANN 2015 - Exploiting the ODD framework to define a novel effective graph kernel [Details]
- ESANN 2016 - Challenges in Deep Learning [Details]
- ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
- ESANN 2017 - Approximated Neighbours MinHash Graph Node Kernel [Details]
- ESANN 2017 - The Conjunctive Disjunctive Node Kernel [Details]
- ESANN 2018 - DEEP: decomposition feature enhancement procedure for graphs [Details]
- ESANN 2019 - Embeddings and Representation Learning for Structured Data [Details]
- ESANN 2019 - On the definition of complex structured feature spaces [Details]
- ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
- ESANN 2021 - Tangent Graph Convolutional Network [Details]
- ESANN 2022 - Biased Edge Dropout in NIFTY for Fair Graph Representation Learning [Details]
- ESANN 2023 - An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features [Details]
- ESANN 2023 - Real-time Detection of Evoked Potentials by Deep Learning: a Case Study [Details]
- ESANN 2024 - Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets [Details]
- ESANN 2025 - D4: Distance Diffusion for a Truly Equivariant Molecular Design [Details]
- ESANN 2020 - A Systematic Assessment of Deep Learning Models for Molecule Generation [Details]
- ESANN 2020 - Deep Recurrent Graph Neural Networks [Details]
- ESANN 2020 - Linear Graph Convolutional Networks [Details]
- ESANN 2014 - Exploiting similarity in system identification tasks with recurrent neural networks [Details]
- ESANN 2017 - Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks [Details]
- ESANN 2024 - Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies [Details]
- ESANN 2023 - Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability [Details]