Electronic proceedings author index

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
João J. G. Lima
  • ESANN 2024 - Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models [Details]
Priscila Lima
  • ESANN 2020 - Detection of elementary particles with the WiSARD n-tuple classifier [Details]
  • ESANN 2020 - Fast Deep Neural Networks Convergence using a Weightless Neural Model [Details]
  • ESANN 2020 - Interpretation of Model Agnostic Classifiers via Local Mental Images [Details]
  • ESANN 2021 - A bag of nodes primer on weightless graph classification [Details]
  • ESANN 2021 - Functional Gradient Descent for n-Tuple Regression [Details]
Priscila Lima
  • No papers found
Priscila M. V. Lima Lima
  • ESANN 2011 - Clustering data streams with weightless neural networks [Details]
Rafael Lima de Carvalho
  • ESANN 2014 - Online tracking of multiple objects using WiSARD [Details]
Aluizio Lima Filho
  • ESANN 2019 - Prediction of palm oil production with an enhanced n-Tuple Regression Network [Details]
Aluizio Lima Filho
  • ESANN 2020 - Interpretation of Model Agnostic Classifiers via Local Mental Images [Details]
Christian Limberg
  • ESANN 2018 - Efficient accuracy estimation for instance-based incremental active learning [Details]
Abdelhakim Limem
  • ESANN 2014 - Enhanced NMF initialization using a physical model for pollution source apportionment [Details]
Mu-Han Lin
  • ESANN 2019 - Application of deep neural networks for automatic planning in radiation oncology treatments [Details]
G. Linan
  • ESANN 2001 - CMOS design of focal plane programmable array processors [Details]
Peggy Lindner
  • ESANN 2021 - Machine Learning for Measuring and Analyzing Online Social Communications [Details]
  • ESANN 2024 - A Two-Stage Approach for Implicit Bias Detection in Generative Language Models [Details]
Peggy Lindner
  • ESANN 2026 - The Alignment Gate: Intent and Instruction Guardrails for Agentic AI [Details]
Gustavo Linhares Vieira
  • ESANN 2018 - An extension of nonstationary fuzzy sets to heteroskedastic fuzzy time series [Details]
Joakim Linja
  • ESANN 2021 - Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces [Details]
  • ESANN 2023 - Feature Selection for Multi-label Classification with Minimal Learning Machine [Details]
A. Linkas
  • ESANN 1993 - Embedding knowledge ibto stochastic learning automata for fast solution ofbinary constraint satisfaction problems [Details]
Derek A. Linkens
  • ESANN 2005 - Artificial intelligence techniques for the prediction of bladder cancer progression [Details]
Jonathan Linton
  • ESANN 2009 - Reservoir computing for static pattern recognition [Details]
  • ESANN 2010 - An augmented efficient backpropagation training strategy for deep autoassociative neural networks [Details]
  • ESANN 2011 - A brief tutorial on reinforcement learning: The game of Chung Toi [Details]
  • ESANN 2012 - Hybrid hierarchical clustering: cluster assessment via cluster validation indices [Details]
  • ESANN 2013 - An empirical analysis of reinforcement learning using design of experiments [Details]
  • ESANN 2013 - Random Brains: An ensemble method for feature selection with neural networks [Details]
Aldo Lipani
  • ESANN 2021 - A Baseline for Shapley Values in MLPs: from Missingness to Neutrality [Details]
  • ESANN 2022 - Attention-based Ingredient Phrase Parser [Details]
  • ESANN 2023 - Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis [Details]
Maria Clara Lippi
  • No papers found
Paulo J. G. Lisboa
  • ESANN 2016 - Performance assessment of quantum clustering in non-spherical data distributions [Details]
  • ESANN 2016 - Physics and Machine Learning: Emerging Paradigms [Details]
Paulo J.G. Lisboa
  • ESANN 2005 - Functional topographic mapping for robust handling of outliers in brain tumour data [Details]
  • ESANN 2005 - Handling outliers and missing data in brain tumour clinical assessment using t-GTM [Details]
  • ESANN 2008 - Machine learning in cancer research: implications for personalised medicine [Details]
  • ESANN 2010 - Computational Intelligence in biomedicine: Some contributions [Details]
Paulo Lisboa
  • ESANN 2021 - The Coming of Age of Interpretable and Explainable Machine Learning Models [Details]
  • ESANN 2021 - The partial response SVM [Details]
Paulo Lisboa
  • ESANN 2006 - Learning what is important: feature selection and rule extraction in a virtual course [Details]
  • ESANN 2011 - Seeing is believing: The importance of visualization in real-world machine learning applications [Details]
  • ESANN 2011 - The role of Fisher information in primary data space for neighbourhood mapping [Details]
  • ESANN 2012 - Constructing similarity networks using the Fisher information metric [Details]
  • ESANN 2012 - Making machine learning models interpretable [Details]
  • ESANN 2013 - Research directions in interpretable machine learning models [Details]
  • ESANN 2015 - Measuring scoring efficiency through goal expectancy estimation [Details]
  • ESANN 2018 - Bioinformatics and medicine in the era of deep learning [Details]
  • ESANN 2019 - Societal Issues in Machine Learning: When Learning from Data is Not Enough [Details]
Michal Lisicki
  • ESANN 2017 - Structure optimization for deep multimodal fusion networks using graph-induced kernels [Details]
E. Littmann
  • ESANN 1996 - Neural model for visual contrast detection [Details]
Hui Liu
  • ESANN 2005 - Computational models of intracytoplasmic sperm injection prognosis [Details]
  • ESANN 2017 - Myoelectrical signal classification based on S transform and two-directional 2DPCA [Details]
Rui Liu
  • ESANN 2024 - Few-shot similarity learning for motion classification via electromyography [Details]
  • ESANN 2025 - Linear Domain Adaptation for Robustness to Electrode Shifts [Details]
Zhengying Liu
  • ESANN 2022 - Filtering participants improves generalization in competitions and benchmarks [Details]
Hong Liu
  • ESANN 2024 - CNNGen: A Generator and a Dataset for Energy-Aware Neural Architecture Search [Details]