A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
S. Ridella
- ESANN 1995 - Learning the appropriate representation paradigm by circular processing units [Details]
- ESANN 2020 - Improving the Union Bound: a Distribution Dependent Approach [Details]
- ESANN 2021 - The Benefits of Adversarial Defence in Generalisation [Details]
- ESANN 2022 - Do We Really Need a New Theory to Understand the Double-Descent? [Details]
- ESANN 2023 - Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective [Details]
- ESANN 2024 - Informed Machine Learning: Excess Risk and Generalization [Details]
- ESANN 2010 - Maximal Discrepancy for Support Vector Machines [Details]
- ESANN 2011 - Maximal Discrepancy vs. Rademacher Complexity for error estimation [Details]
- ESANN 2012 - Structural Risk Minimization and Rademacher Complexity for Regression [Details]
- ESANN 2012 - The `K' in K-fold Cross Validation [Details]
- ESANN 2013 - A Learning Machine with a Bit-Based Hypothesis Space [Details]
- ESANN 2014 - Learning with few bits on small-scale devices: From regularization to energy efficiency [Details]
- ESANN 2016 - Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data [Details]
- ESANN 2017 - Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions [Details]
- ESANN 2018 - Local Rademacher Complexity Machine [Details]
- ESANN 2012 - Hidden Markov models for time series of counts with excess zeros [Details]
- ESANN 2012 - Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces [Details]
- ESANN 2013 - Regularization in relevance learning vector quantization using l1-norms [Details]
- ESANN 2023 - Energy-efficient detection of a spike sequence [Details]
- ESANN 1997 - Application of a self-learning controller with continuous control signals based on the DOE-approach [Details]
- ESANN 2006 - Reducing policy degradation in neuro-dynamic programming [Details]
- ESANN 2007 - Reinforcement learning in a nutshell [Details]
- ESANN 2010 - Deep learning of visual control policies [Details]
- ESANN 2013 - Optimization of Gaussian process hyperparameters using Rprop [Details]
- ESANN 2018 - Controlling biological neural networks with deep reinforcement learning [Details]
- ESANN 2020 - Verifying Deep Learning-based Decisions for Facial Expression Recognition [Details]
- ESANN 2020 - A Survey of Machine Learning applied to Computer Networks [Details]
- ESANN 2023 - Potential analysis of a Quantum RL controller in the context of autonomous driving [Details]
- ESANN 1999 - From regression to classification in support vector machines [Details]
- ESANN 2012 - learning task relatedness via dirichlet process priors for linear regression models [Details]
- ESANN 1998 - Speech recognition with a new hybrid architecture combining neural networks and continuous HMM [Details]
- ESANN 2020 - A Systematic Assessment of Deep Learning Models for Molecule Generation [Details]
- ESANN 2020 - Exploring the feature space of character-level embeddings [Details]
- ESANN 2017 - Prediction of preterm infant mortality with Gaussian process classification [Details]
- ESANN 2023 - Multimodal Recognition of Valence, Arousal and Dominance via Late-Fusion of Text, Audio and Facial Expressions [Details]
- ESANN 2024 - Towards Contrail Mitigation through Robust and Frugal AI-Driven Data Exploitation [Details]
- ESANN 1999 - Development of a French speech recognizer using a hybrid HMM/MLP system [Details]
- ESANN 2013 - Dimension reduction for individual ica to decompose FMRI during real-world experiences: principal component analysis vs. canonical correlation analysis [Details]
- ESANN 2011 - D-VisionDraughts: a draughts player neural network that learns by reinforcement in a high performance environment [Details]
- ESANN 1999 - Feature binding and relaxation labeling with the competitive layer model [Details]
- ESANN 1999 - Maximisation of stability ranges for recurrent neural networks subject to on-line adaptation [Details]
- ESANN 2000 - A neural network approach to adaptive pattern analysis - the deformable feature map [Details]
- ESANN 2000 - A neural network architecture for automatic segmentation of fluorescence micrographs [Details]
- ESANN 2002 - Combining gestural and contact information for visual guidance of multi-finger grasps [Details]
- ESANN 2003 - Semi-automatic acquisition and labelling of image data using SOMs [Details]
- ESANN 2005 - Relevance determination in reinforcement learning [Details]
- ESANN 2006 - Adaptive scene-dependent filters in online learning environments [Details]
- ESANN 2006 - Variants of Unsupervised Kernel Regression: General cost functions [Details]
- ESANN 2010 - Neural competition for motion segmentation [Details]
- ESANN 2013 - Perceptual grouping through competition in coupled oscillator networks [Details]
- ESANN 2018 - Efficient accuracy estimation for instance-based incremental active learning [Details]
- ESANN 2019 - Conditional WGAN for grasp generation [Details]
- ESANN 2011 - D-VisionDraughts: a draughts player neural network that learns by reinforcement in a high performance environment [Details]
- ESANN 2003 - Characterization of the absolutely expedient learning algorithms for stochastic automata in a non-discrete space of actions [Details]
- ESANN 2021 - Fusion of estimations from two modalities using the Viterbi's algorithm: application to fetal heart rate monitoring [Details]
- ESANN 2009 - Sensors selection for P300 speller brain computer interface [Details]
- ESANN 2012 - Application of Dynamic Time Warping on Kalman Filtering Framework for Abnormal ECG Filtering [Details]
- ESANN 2001 - Applications of neuro-fuzzy classification, evaluation and forecasting techniques in agriculture [Details]