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Luiz Oliveira
- ESANN 2020 - Interpretation of Model Agnostic Classifiers via Local Mental Images [Details]
- ESANN 1997 - Scene categorisation by curvilinear component analysis of low frequency spectra [Details]
- ESANN 2021 - Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability? [Details]
- No papers found
- ESANN 2022 - Challenges in anomaly and change point detection [Details]
- ESANN 2007 - Estimating the Number of Components in a Mixture of Multilayer Perceptrons [Details]
- ESANN 2009 - Supervised classification of categorical data with uncertain labels for DNA barcoding [Details]
- ESANN 2010 - Asymptotic properties of mixture-of-experts models [Details]
- ESANN 2012 - Hidden Markov models for time series of counts with excess zeros [Details]
- ESANN 2013 - Multiple Kernel Self-Organizing Maps [Details]
- ESANN 2016 - Comparison of three algorithms for parametric change-point detection [Details]
- ESANN 2017 - Accelerating stochastic kernel SOM [Details]
- ESANN 2019 - Analyzing spatial dissimilarities in high-resolution geo-data : a case study of four European cities [Details]
- ESANN 2020 - Sparse K-means for mixed data via group-sparse clustering [Details]
- No papers found
- ESANN 2008 - Nonlinear data projection on a sphere with controlled trade-off between trustworthiness and continuity [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 2013 - A Public Domain Dataset for Human Activity Recognition using Smartphones [Details]
- ESANN 2014 - Byte The Bullet: Learning on Real-World Computing Architectures [Details]
- ESANN 2014 - Learning with few bits on small-scale devices: From regularization to energy efficiency [Details]
- ESANN 2015 - Advances in learning analytics and educational data mining [Details]
- ESANN 2015 - Human Algorithmic Stability and Human Rademacher Complexity [Details]
- ESANN 2015 - Model Selection for Big Data: Algorithmic Stability and Bag of Little Bootstraps on GPUs [Details]
- ESANN 2016 - Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints [Details]
- ESANN 2016 - Measuring the Expressivity of Graph Kernels through the Rademacher Complexity [Details]
- ESANN 2016 - Random Forests Model Selection [Details]
- ESANN 2016 - Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data [Details]
- ESANN 2017 - Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach [Details]
- ESANN 2017 - Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions [Details]
- ESANN 2018 - Emerging trends in machine learning: beyond conventional methods and data [Details]
- ESANN 2018 - Local Rademacher Complexity Machine [Details]
- ESANN 2019 - Fairness and Accountability of Machine Learning Models in Railway Market: are Applicable Railway Laws Up to Regulate Them? [Details]
- ESANN 2019 - PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors [Details]
- ESANN 2019 - Societal Issues in Machine Learning: When Learning from Data is Not Enough [Details]
- ESANN 2020 - Improving the Union Bound: a Distribution Dependent Approach [Details]
- ESANN 2020 - Learning Deep Fair Graph Neural Networks [Details]
- ESANN 2021 - Complex Data: Learning Trustworthily, Automatically, and with Guarantees [Details]
- ESANN 2021 - In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models [Details]
- ESANN 2021 - The Benefits of Adversarial Defence in Generalisation [Details]
- ESANN 2022 - Biased Edge Dropout in NIFTY for Fair Graph Representation Learning [Details]
- ESANN 2022 - Do We Really Need a New Theory to Understand the Double-Descent? [Details]
- ESANN 2022 - Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets [Details]
- ESANN 2023 - An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features [Details]
- ESANN 2023 - Improving Fairness via Intrinsic Plasticity in Echo State Networks [Details]
- ESANN 2023 - Mitigating Robustness Bias: Theoretical Results and Empirical Evidences [Details]
- ESANN 2023 - Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective [Details]
- ESANN 2024 - Informed Machine Learning for Complex Data [Details]
- ESANN 2024 - Informed Machine Learning: Excess Risk and Generalization [Details]
- ESANN 2018 - Active Learning based on Transfer Learning Techniques for Image Classification [Details]
- ESANN 2020 - A preconditioned accelerated stochastic gradient descent algorithm [Details]
- ESANN 2024 - Transfer learning to minimize the predictive risk in clinical research [Details]
- ESANN 2023 - Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis [Details]
- ESANN 1995 - Improving object recognition by using a visual latency mechanism [Details]
- ESANN 2004 - Modelling of biologically plausible excitatory networks: emergence and modulation of neural synchrony [Details]
- ESANN 2004 - Fast semi-automatic segmentation algorithm for Self-Organizing Maps [Details]
- ESANN 2019 - Statistical physics of learning and inference [Details]
- ESANN 2013 - Unsupervised non-linear neural networks capture aspects of floral choice behaviour [Details]
- ESANN 2016 - PSCEG: an unbiased parallel subspace clustering algorithm using exact grids [Details]
- ESANN 2016 - Parallelized unsupervised feature selection for large-scale network traffic analysis [Details]
- ESANN 2017 - Deep convolutional neural networks for detecting noisy neighbours in cloud infrastructure [Details]
- ESANN 2005 - A Neural Network that helps building a Nonlinear Dynamical model of a Power Amplifier [Details]
- ESANN 2016 - Random Forests Model Selection [Details]
- ESANN 2000 - Discriminative learning for neural decision feedback equalizers [Details]
- ESANN 2014 - Lightning fast asynchronous distributed k-means clustering [Details]
- ESANN 2003 - Detecting Pathologies from Infant Cry Applying Scaled Conjugated Gradient Neural Networks [Details]
- ESANN 1995 - Radial basis functions in the Fourier domain [Details]
- ESANN 2011 - SO-VAT: Self-Organizing Visual Assessment of cluster Tendency for large data sets [Details]
- ESANN 2012 - magnitude sensitive competitive learning [Details]
- ESANN 2002 - Evaluating the impact of multiplicative input perturbations on radial basis function networks [Details]
- ESANN 2002 - Orthogonal transformations for optimal time series prediction [Details]
- ESANN 2021 - Comprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Devices [Details]