Learning long-term dependencies is not as difficult with NARX networks. - Tsungnan Lin; Bill G. Horne; Peter Tiño; C. Lee Giles (NIPS, 1995)

Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model. - Peter Tiño; Jozef Sajda (Neural Comput., 1995)

Learning long-term dependencies in NARX recurrent neural networks. - Tsungnan Lin; Bill G. Horne; Peter Tiño; C. Lee Giles (IEEE Trans. Neural Networks, 1996)

Modeling Complex Symbolic Sequences with Neural Based Systems. - Peter Tiño; V. Vojtek (ICANNGA, 1997)

Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences. - Peter Tiño; V. Vojtek (KES (2), 1997)

Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics. - Peter Tiño; Georg Dorffner; Christian Schittenkopf (Hybrid Neural Systems, 1998)

Recurrent Neural Networks with Iterated Function Systems Dynamics. - Peter Tiño; Georg Dorffner (NC, 1998)

Graded Grammaticality in Prediction Fractal Machines. - Shan Parfitt; Peter Tiño; Georg Dorffner (NIPS, 1999)

Building Predictive Models from Fractal Representations of Symbolic Sequences. - Peter Tiño; Georg Dorffner (NIPS, 1999)

Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences. - Peter Tiño; Miroslav Koteles (IEEE Trans. Neural Networks, 1999)

Spatial representation of symbolic sequences through iterative function systems. - Peter Tiño (IEEE Trans. Syst. Man Cybern. Part A, 1999)

The profitability of trading volatility using real-valued and symbolic models. - Christian Schittenkopf; Peter Tiño; Georg Dorffner (CIFEr, 2000)

Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition. - Peter Tiño; Michal Stancík; Lubica Benusková (IJCNN (2), 2000)

Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds. - Peter Tiño; Ian T. Nabney; Yi Sun 001 (ICANN, 2001)

Predicting the Future of Discrete Sequences from Fractal Representations of the Past. - Peter Tiño; Georg Dorffner (Mach. Learn., 2001)

Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks). - Peter Tiño; Bill G. Horne; C. Lee Giles (Neural Comput., 2001)

Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series. - Peter Tiño; Christian Schittenkopf; Georg Dorffner (Pattern Anal. Appl., 2001)

Financial volatility trading using recurrent neural networks. - Peter Tiño; Christian Schittenkopf; Georg Dorffner (IEEE Trans. Neural Networks, 2001)

A General Framework for a Principled Hierarchical Visualization of Multivariate Data. - Ata Kabán; Peter Tiño; Mark A. Girolami (IDEAL, 2002)

Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way. - Peter Tiño; Ian T. Nabney (IEEE Trans. Pattern Anal. Mach. Intell., 2002)

Recurrent Neural Networks with Small Weights Implement Definite Memory Machines. - Barbara Hammer; Peter Tiño (Neural Comput., 2003)

Architectural Bias in Recurrent Neural Networks: Fractal Analysis. - Peter Tiño; Barbara Hammer (Neural Comput., 2003)

On Early Stages of Learning in Connectionist Models with Feedback Connections. - Peter Tiño; Barbara Hammer (AAAI Technical Report (3), 2004)

Introducing a Star Topology into Latent Class Models for Collaborative Filtering. - Gabriela Polcicová; Peter Tiño (AIAI, 2004)

A generative probabilistic approach to visualizing sets of symbolic sequences. - Peter Tiño; Ata Kabán; Yi Sun 001 (KDD, 2004)

Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies. - Richard Price; Peter Tiño (PPSN, 2004)

Nonlinear Prediction of Quantitative Structure-Activity Relationships. - Peter Tiño; Ian T. Nabney; Bruce S. Williams; Jens Lösel; Yi Sun 001 (J. Chem. Inf. Model., 2004)

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns. - Gabriela Polcicová; Peter Tiño (Neural Networks, 2004)

Markovian architectural bias of recurrent neural networks. - Peter Tiño; Michal Cernanský; Lubica Benusková (IEEE Trans. Neural Networks, 2004)

On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map. - Peter Tiño; Igor Farkas (ICNC (2), 2005)

Recursive Self-organizing Map as a Contractive Iterative Function System. - Peter Tiño; Igor Farkas; Jort van Mourik (IDEAL, 2005)

Sequential relevance vector machine learning from time series. - Nikolay I. Nikolaev; Peter Tino (IJCNN, 2005)

Managing Diversity in Regression Ensembles. - Gavin Brown 001; Jeremy L. Wyatt; Peter Tiño (J. Mach. Learn. Res., 2005)

Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization. - Ian T. Nabney; Yi Sun 001; Peter Tiño; Ata Kabán (IEEE Trans. Knowl. Data Eng., 2005)

A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses. - Juan Carlos Cuevas-Tello; Peter Tiño; Somak Raychaudhury (ECML, 2006)

A Probabilistic Ensemble Pruning Algorithm. - Huanhuan Chen; Peter Tiño; Xin Yao 001 (ICDM Workshops, 2006)

Does Money Matter? An Artificial Intelligence Approach. - Jane M. Binner; Barry Jones; Graham Kendall; Jonathan A. Tepper; Peter Tiño (JCIS, 2006)

Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. - Peter Tiño (PPSN, 2006)

How accurate are the time delay estimates in gravitational lensing? - Juan Carlos Cuevas-Tello; Peter Tiño; Somak Raychaudhury (CoRR, 2006)

Dynamics and Topographic Organization of Recursive Self-Organizing Maps. - Peter Tiño; Igor Farkas; Jort van Mourik (Neural Comput., 2006)

Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons. - Peter Tiño; Ashley J. S. Mills (Neural Comput., 2006)

Visualisation of tree-structured data through generative probabilistic modelling. - Nikolaos Gianniotis; Peter Tiño (ESANN, 2007)

Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences. - Michal Cernanský; Peter Tiño (ICANN (1), 2007)

Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks. - Peter Tiño (ICONIP (1), 2007)

Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling. - Peter Tiño; Nikolaos Gianniotis (IJCAI, 2007)

On Conditions for Intermittent Search in Self-organizing Neural Networks. - Peter Tiño (MICAI, 2007)

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks. - Peter Tiño (Neural Comput., 2007)

Markovian Bias of Neural-based Architectures With Feedback Connections. - Peter Tiño; Barbara Hammer; Mikael Bodén (Perspectives of Neural-Symbolic Integration, 2007)

Predictive Modeling with Echo State Networks. - Michal Cernanský; Peter Tiño (ICANN (1), 2008)

Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model. - Xiaoxia Wang; Peter Tiño; Mark A. Fardal (ECML/PKDD (2), 2008)

Measuring Generalization Performance in Coevolutionary Learning. - Siang Yew Chong; Peter Tiño; Xin Yao 001 (IEEE Trans. Evol. Comput., 2008)

Visualization of Tree-Structured Data Through Generative Topographic Mapping. - Nikolaos Gianniotis; Peter Tiño (IEEE Trans. Neural Networks, 2008)

Still alive: Extending keep-alive intervals in P2P overlay networks. - Richard Price; Peter Tiño (CollaborateCom, 2009)

Visualization of Structured Data via Generative Probabilistic Modeling. - Nikolaos Gianniotis; Peter Tiño (Similarity-Based Clustering, 2009)

Estimating Time Delay in Gravitationally Lensed Fluxes. - Peter Tiño; Juan Carlos Cuevas-Tello; Somak Raychaudhury (Similarity-based learning on structures, 2009)

Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. - Nikolaos Gianniotis; Peter Tiño; Steve Spreckley; Somak Raychaudhury (ICANN (1), 2009)

Probabilistic Model Based Hough Transform for Detection of Co-expression Patterns in Three-Color cDNA Microarray Data. - Peter Tiño; Hongya Zhao; Hong Yan 001 (IJCBS, 2009)

Fast parzen window density estimator. - Xiaoxia Wang; Peter Tiño; Mark A. Fardal; Somak Raychaudhury; Arif Babul (IJCNN, 2009)

Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays. - Peter Tiño (BMC Bioinform., 2009)

Relationship Between Generalization and Diversity in Coevolutionary Learning. - Siang Yew Chong; Peter Tiño; Xin Yao 001 (IEEE Trans. Comput. Intell. AI Games, 2009)

Predictive Ensemble Pruning by Expectation Propagation. - Huanhuan Chen; Peter Tiño; Xin Yao 001 (IEEE Trans. Knowl. Data Eng., 2009)

Probabilistic Classification Vector Machines. - Huanhuan Chen; Peter Tiño; Xin Yao 001 (IEEE Trans. Neural Networks, 2009)

One-shot Learning of Poisson Distributions in fast changing environments. - Peter Tiño (Learning paradigms in dynamic environments, 2010)

On Reliability Of Simulations Of Complex Co-Evolutionary Processes. - Peter Tiño; Siang Yew Chong; Xin Yao 001 (ECMS, 2010)

Multilinear Decomposition and Topographic Mapping of Binary Tensors. - Jakub Mazgut; Peter Tiño; Mikael Bodén; Hong Yan 001 (ICANN (1), 2010)

Simple Deterministically Constructed Recurrent Neural Networks. - Ali Rodan; Peter Tiño (IDEAL, 2010)

Adapting to NAT timeout values in P2P overlay networks. - Richard Price; Peter Tiño (IPDPS Workshops, 2010)

Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application. - Juan Carlos Cuevas-Tello; Peter Tiño; Somak Raychaudhury; Xin Yao 001; Markus Harva (Pattern Recognit., 2010)

Negatively Correlated Echo State Networks. - Ali Rodan; Peter Tiño (ESANN, 2011)

Time-Dependent Series Variance Estimation via Recurrent Neural Networks. - Nikolay I. Nikolaev; Peter Tiño; Evgueni N. Smirnov (ICANN (1), 2011)

Real-Time Detection of Process Change using Process Mining. - Phil Weber; Behzad Bordbar; Peter Tiño (ICCSW, 2011)

A Principled Approach to the Analysis of Process Mining Algorithms. - Phil Weber; Behzad Bordbar; Peter Tiño (IDEAL, 2011)

One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression. - Peter Tiño (ISNN (2), 2011)

Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis. - Jakub Mazgut; Martina Paulinyová; Peter Tiño (MEMICS, 2011)

Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform. - Peter Tiño; Hongya Zhao; Hong Yan 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

Minimum Complexity Echo State Network. - Ali Rodan; Peter Tiño (IEEE Trans. Neural Networks, 2011)

Theory of Input Driven Dynamical Systems. - Manjunath Gandhi; Peter Tiño; Herbert Jaeger (ESANN, 2012)

Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems. - Peter Tiño; Ali Rodan (ESANN, 2012)

Process Mining in Non-Stationary Environments. - Phil Weber; Peter Tiño; Behzad Bordbar (ESANN, 2012)

Learning Using Privileged Information in Prototype Based Models. - Shereen Fouad; Peter Tiño; Somak Raychaudhury; Petra Schneider (ICANN (2), 2012)

Prototype Based Modelling for Ordinal Classification. - Shereen Fouad; Peter Tiño (IDEAL, 2012)

Computational Intelligence in Astronomy - A Win-Win Situation. - Peter Tiño; Somak Raychaudhury (TPNC, 2012)

Learning in the Model Space for Fault Diagnosis - Huanhuan Chen; Peter Tiño; Xin Yao 001; Ali Rodan (CoRR, 2012)

Still Alive: Extending Keep-Alive Intervals in P2P Overlay Networks. - Richard Price; Peter Tiño; Georgios Theodoropoulos 001 (Mob. Networks Appl., 2012)

Adaptive Metric Learning Vector Quantization for Ordinal Classification. - Shereen Fouad; Peter Tiño (Neural Comput., 2012)

Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps. - Ali Rodan; Peter Tiño (Neural Comput., 2012)

Improving Generalization Performance in Co-Evolutionary Learning. - Siang Yew Chong; Peter Tiño; Day Chyi Ku; Xin Yao 001 (IEEE Trans. Evol. Comput., 2012)

A principled approach to mining from noisy logs using Heuristics Miner. - Phil Weber; Behzad Bordbar; Peter Tiño (CIDM, 2013)

Ordinal-based metric learning for learning using privileged information. - Shereen Fouad; Peter Tiño (IJCNN, 2013)

Concept drift detection for online class imbalance learning. - Shuo Wang 005; Leandro L. Minku; Davide Ghezzi; Daniele Caltabiano; Peter Tiño; Xin Yao 001 (IJCNN, 2013)

Model-based kernel for efficient time series analysis. - Huanhuan Chen; Fengzhen Tang; Peter Tiño; Xin Yao 001 (KDD, 2013)

A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data. - Yuan Shen; Stephen D. Mayhew; Zoe Kourtzi; Peter Tiño (MICCAI (2), 2013)

Degree distribution and scaling in the Connecting Nearest Neighbors model - Boris Rudolf; Mária Markosová; Martin Cajági; Peter Tiño (CoRR, 2013)

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get? - Peter Tiño (Entropy, 2013)

Novel approaches in machine learning and computational intelligence. - Alessio Micheli; Frank-Michael Schleif; Peter Tiño (Neurocomputing, 2013)

Time-dependent series variance learning with recurrent mixture density networks. - Nikolay I. Nikolaev; Peter Tiño; Evgueni N. Smirnov (Neurocomputing, 2013)

Short term memory in input-driven linear dynamical systems. - Peter Tiño; Ali Rodan (Neurocomputing, 2013)

Exploitation of Pairwise Class Distances for Ordinal Classification. - Javier Sánchez-Monedero; Pedro Antonio Gutiérrez; Peter Tiño; César Hervás-Martínez (Neural Comput., 2013)

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models. - Orla M. Doyle; Krasimira Tsaneva-Atanasova; James Michael Harte; Paul A. Tiffin; Peter Tiño; Vanessa Díaz-Zuccarini (IEEE Trans. Biomed. Eng., 2013)

Scaling Up Estimation of Distribution Algorithms for Continuous Optimization. - Weishan Dong; Tianshi Chen 002; Peter Tiño; Xin Yao 001 (IEEE Trans. Evol. Comput., 2013)

Complex Coevolutionary Dynamics - Structural Stability and Finite Population Effects. - Peter Tiño; Siang Yew Chong; Xin Yao 001 (IEEE Trans. Evol. Comput., 2013)

Incorporating Privileged Information Through Metric Learning. - Shereen Fouad; Peter Tiño; Somak Raychaudhury; Petra Schneider (IEEE Trans. Neural Networks Learn. Syst., 2013)

A Framework for the Analysis of Process Mining Algorithms. - Phil Weber; Behzad Bordbar; Peter Tiño (IEEE Trans. Syst. Man Cybern. Syst., 2013)

Recent trends in learning of structured and non-standard data. - Frank-Michael Schleif; Peter Tiño; Thomas Villmann (ESANN, 2014)

Support Vector Ordinal Regression using Privileged Information. - Fengzhen Tang; Peter Tiño; Pedro Antonio Gutiérrez; Huanhuan Chen (ESANN, 2014)

Towards Self-Aware Service Composition. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tiño (HPCC/CSS/ICESS, 2014)

Learning the deterministically constructed Echo State Networks. - Fengzhen Tang; Peter Tiño; Huanhuan Chen (IJCNN, 2014)

A Utility Model for Volunteered Service Composition. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tiño; Peter R. Lewis 001 (UCC, 2014)

Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space. - Huanhuan Chen; Peter Tiño; Xin Yao 001 (Comput. Chem. Eng., 2014)

Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network. - Joseba Quevedo; Huanhuan Chen; Miquel Àngel Cugueró; Peter Tiño; Vicenç Puig; Diego García; Ramon Sarrate; Xin Yao 001 (Eng. Appl. Artif. Intell., 2014)

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models. - Yuan Shen; Stephen D. Mayhew; Zoe Kourtzi; Peter Tiño (NeuroImage, 2014)

Ordinal regression neural networks based on concentric hyperspheres. - Pedro Antonio Gutiérrez; Peter Tiño; César Hervás-Martínez (Neural Networks, 2014)

Dimensionality reduction and topographic mapping of binary tensors. - Jakub Mazgut; Peter Tiño; Mikael Bodén; Hong Yan 001 (Pattern Anal. Appl., 2014)

Learning in the Model Space for Cognitive Fault Diagnosis. - Huanhuan Chen; Peter Tiño; Ali Rodan; Xin Yao 001 (IEEE Trans. Neural Networks Learn. Syst., 2014)

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection. - Huanhuan Chen; Peter Tiño; Xin Yao 001 (IEEE Trans. Neural Networks Learn. Syst., 2014)

Autoencoding time series for visualisation. - Nikolaos Gianniotis; Sven Dennis Kügler; Peter Tiño; Kai Polsterer; Ranjeev Misra (ESANN, 2015)

Probabilistic Classification Vector Machine at large scale. - Frank-Michael Schleif; Andrej Gisbrecht; Peter Tiño (ESANN, 2015)

Automated Detection of Galaxy Groups Through Probabilistic Hough Transform. - Rafee T. Ibrahem; Peter Tiño; Richard J. Pearson; Trevor J. Ponman; Arif Babul (ICONIP (3), 2015)

Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tiño; Peter R. Lewis 001 (ICWS, 2015)

Model Metric Co-Learning for Time Series Classification. - Huanhuan Chen; Fengzhen Tang; Peter Tiño; Anthony G. Cohn 001; Xin Yao 001 (IJCAI, 2015)

Incremental probabilistic classification vector machine with linear costs. - Frank-Michael Schleif; H. Chen; Peter Tiño (IJCNN, 2015)

Large Scale Indefinite Kernel Fisher Discriminant. - Frank-Michael Schleif; Andrej Gisbrecht; Peter Tiño (SIMBAD, 2015)

Indefinite Proximity Learning: A Review. - Frank-Michael Schleif; Peter Tiño (Neural Comput., 2015)

The Benefits of Modeling Slack Variables in SVMs. - Fengzhen Tang; Peter Tiño; Pedro Antonio Gutiérrez; Huanhuan Chen (Neural Comput., 2015)

Artificial Neural Network Models. - Peter Tiño; Lubica Benuskova; Alessandro Sperduti (Handbook of Computational Intelligence, 2015)

Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. - Frank-Michael Schleif; Ata Kabán; Peter Tiño (ANNPR, 2016)

Learning in indefinite proximity spaces - recent trends. - Frank-Michael Schleif; Peter Tiño; Yingyu Liang (ESANN, 2016)

Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams. - Sultanah Al Otaibi; Peter Tiño; Somak Raychaudhury (IDEAL, 2016)

Interaction-Awareness for Self-Adaptive Volunteer Computing. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tiño (SASO, 2016)

Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data. - Nahed Alowadi; Yuan Shen; Peter Tiño (WSOM, 2016)

Probabilistic classifiers with low rank indefinite kernels. - Frank-Michael Schleif; Andrej Gisbrecht; Peter Tiño (CoRR, 2016)

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment. - Hanin H. Alahmadi; Yuan Shen; Shereen Fouad; Caroline Di Bernardi Luft; Peter Bentham; Zoe Kourtzi; Peter Tiño (Frontiers Comput. Neurosci., 2016)

Model-coupled autoencoder for time series visualisation. - Nikolaos Gianniotis; Sven Dennis Kügler; Peter Tiño; Kai Lars Polsterer (Neurocomputing, 2016)

Oversampling the Minority Class in the Feature Space. - María Pérez-Ortiz 001; Pedro Antonio Gutiérrez; Peter Tiño; César Hervás-Martínez (IEEE Trans. Neural Networks Learn. Syst., 2016)

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Rick van Veen; Gert-Jan de Vries; Michael Biehl; Wiebke Arlt; Peter Tiño; Kerstin Bunte (ESANN, 2017)

Fisher memory of linear Wigner echo state networks. - Peter Tiño (ESANN, 2017)

Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tiño (ICWS, 2017)

Linear dynamical based models for sequential domains. - Luca Pasa; Alessandro Sperduti; Peter Tiño (IJCNN, 2017)

Classification of sparsely and irregularly sampled time series: A learning in model space approach. - Yuan Shen; Peter Tiño; Krasimira Tsaneva-Atanasova (IJCNN, 2017)

Probabilistic matching: Causal inference under measurement errors. - Fani Tsapeli; Peter Tiño; Mirco Musolesi (IJCNN, 2017)

Linking Twitter Events With Stock Market Jitters. - Fani Tsapeli; Nikolaos Bezirgiannidis; Peter Tiño; Mirco Musolesi (CoRR, 2017)

Ordinal regression based on learning vector quantization. - Fengzhen Tang; Peter Tiño (Neural Networks, 2017)

Indefinite Core Vector Machine. - Frank-Michael Schleif; Peter Tiño (Pattern Recognit., 2017)

Machine learning and data analysis in astroinformatics. - Michael Biehl; Kerstin Bunte; Giuseppe Longo; Peter Tiño (ESANN, 2018)

Randomized Recurrent Neural Networks. - Claudio Gallicchio; Alessio Micheli; Peter Tiño (ESANN, 2018)

A mixture of experts model for predicting persistent weather patterns. - M. Pérez-Ortiz 001; Pedro Antonio Gutiérrez; Peter Tiño; Carlos Casanova-Mateo; Sancho Salcedo-Sanz (IJCNN, 2018)

Sparsification of Indefinite Learning Models. - Frank-Michael Schleif; Christoph Raab; Peter Tiño (S+SSPR, 2018)

Supervised low rank indefinite kernel approximation using minimum enclosing balls. - Frank-Michael Schleif; Andrej Gisbrecht; Peter Tiño (Neurocomputing, 2018)

Asymptotic Fisher memory of randomized linear symmetric Echo State Networks. - Peter Tiño (Neurocomputing, 2018)

Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. - María Pérez-Ortiz 001; Peter Tiño; Rafal Mantiuk; César Hervás-Martínez (AAAI, 2019)

Multiobjective Optimization Approach to Localization of Ectopic Beats by Single Dipole: Case Study. - Jana Svehlíková; Jan Zelinka; Milan Tysler; Peter Tiño (CinC, 2019)

Feature relevance bounds for ordinal regression. - Lukas Pfannschmidt; Jonathan Jakob; Michael Biehl; Peter Tiño; Barbara Hammer (ESANN, 2019)

Extended stochastic derivative-free optimization on riemannian manifolds. - Robert Simon Fong; Peter Tiño (GECCO (Companion), 2019)

Learning Transferable Variation Operators in a Continuous Genetic Algorithm. - Stephen Friess; Peter Tiño; Stefan Menzel; Bernhard Sendhoff; Xin Yao 001 (SSCI, 2019)

Coevolutionary systems and PageRank. - Siang Yew Chong; Peter Tiño; Jun He 004 (Artif. Intell., 2019)

A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks. - Siang Yew Chong; Peter Tiño; Jun He 004; Xin Yao 001 (Evol. Comput., 2019)

Self-awareness in Software Engineering: A Systematic Literature Review. - Abdessalam Elhabbash; Maria Salama; Rami Bahsoon; Peter Tiño (ACM Trans. Auton. Adapt. Syst., 2019)

Representing Experience in Continuous Evolutionary optimisation through Problem-tailored Search Operators. - Stephen Friess; Peter Tiño; Stefan Menzel; Bernhard Sendhoff; Xin Yao 001 (CEC, 2020)

ASAP - A Sub-sampling Approach for Preserving Topological Structures. - Abolfazl Taghribi; Kerstin Bunte; Michele Mastropietro; Sven De Rijcke; Peter Tiño (ESANN, 2020)

Visualisation and knowledge discovery from interpretable models. - Sreejita Ghosh; Peter Tiño; Kerstin Bunte (IJCNN, 2020)

Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances. - Stephen Friess; Peter Tiño; Stefan Menzel; Bernhard Sendhoff; Xin Yao 001 (PPSN (1), 2020)

Input representation in recurrent neural networks dynamics. - Pietro Verzelli; Cesare Alippi; Lorenzo Livi; Peter Tiño (CoRR, 2020)

LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density. - Abolfazl Taghribi; Kerstin Bunte; Rory Smith; Jihye Shin; Michele Mastropietro; Reynier F. Peletier; Peter Tiño (CoRR, 2020)

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals. - Tom Goodman; Karoline van Gemst; Peter Tiño (CoRR, 2020)

Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. - Lukas Pfannschmidt; Jonathan Jakob; Fabian Hinder; Michael Biehl; Peter Tiño; Barbara Hammer (Neurocomputing, 2020)

A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System. - Seyma Kucukozer Cavdar; Tugba Taskaya-Temizel; Mirco Musolesi; Peter Tiño (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2020)

Dynamical Systems as Temporal Feature Spaces. - Peter Tiño (J. Mach. Learn. Res., 2020)

Sparsification of core set models in non-metric supervised learning. - Frank-Michael Schleif; Christoph Raab; Peter Tiño (Pattern Recognit. Lett., 2020)

Model-Based Relevance of Measuring Electrodes for the Inverse Solution with a Single Dipole. - Beata Ondrusova; Jana Svehlíková; Jan Zelinka; Milan Tysler; Peter Tino (CinC, 2021)

Attaining Meta-self-awareness through Assessment of Quality-of-Knowledge. - Abdessalam Elhabbash; Rami Bahsoon; Peter Tino; Peter R. Lewis 001; Yehia Elkhatib (ICWS, 2021)

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations. - Marco Canducci; Abolfazl Taghribi; Michele Mastropietro; Sven De Rijcke; Reynier Peletier; Kerstin Bunte; Peter Tino (IDEAL, 2021)

SOMiMS - Topographic Mapping in the Model Space. - Xinyue Chen; Yuan Shen; Eder Zavala; Krasimira Tsaneva-Atanasova; Thomas Upton; Georgina Russell; Peter Tino (IDEAL, 2021)

Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization. - Stephen Friess; Peter Tiño; Zhao Xu 001; Stefan Menzel; Bernhard Sendhoff; Xin Yao 001 (IJCNN, 2021)

Interpreting Node Embedding with Text-labeled Graphs. - Giuseppe Serra 002; Zhao Xu 001; Mathias Niepert; Carolin Lawrence; Peter Tiño; Xin Yao 001 (IJCNN, 2021)

Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection. - Shuyi Zhang; Chao Pan 005; Liyan Song; Xiaoyu Wu; Zheng Hu; Ke Pei; Peter Tino; Xin Yao 001 (ECML/PKDD (3), 2021)

Predicting CMA-ES Operators as Inductive Biases for Shape Optimization Problems. - Stephen Friess; Peter Tino; Stefan Menzel; Bernhard Sendhoff; Xin Yao 001 (SSCI, 2021)

Designing Robust Models for Behaviour Prediction Using Sparse Data from Mobile Sensing: A Case Study of Office Workers' Availability for Well-being Interventions. - Seyma Kucukozer Cavdar; Tugba Taskaya-Temizel; Abhinav Mehrotra; Mirco Musolesi; Peter Tino (ACM Trans. Comput. Heal., 2021)

Probabilistic learning vector quantization on manifold of symmetric positive definite matrices. - Fengzhen Tang; Haifeng Feng; Peter Tino; Bailu Si; Daxiong Ji (Neural Networks, 2021)

A Survey on Neural Network Interpretability. - Yu Zhang 111; Peter Tiño; Ales Leonardis; Ke Tang 001 (IEEE Trans. Emerg. Top. Comput. Intell., 2021)

Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices. - Fengzhen Tang; Mengling Fan; Peter Tiño (IEEE Trans. Neural Networks Learn. Syst., 2021)

Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical Systems. - Hayatullahi Bolaji Adeyemo; Rami Bahsoon; Peter Tiño (BDCAT, 2022)

Greedy Selection of the Torso Electrodes for the Solution of Inverse Problem with a Single Dipole. - Beata Ondrusova; Jana Svehlíková; Milan Tysler; Peter Tino (CinC, 2022)

Duplication Scheduling with Bottom-Up Top-Down Recursive Neural Network. - Vahab Samandi; Peter Tino; Rami Bahsoon (IDEAL, 2022)

Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction. - Stephen Friess; Peter Tino; Stefan Menzel; Zhao Xu 001; Bernhard Sendhoff; Xin Yao 001 (IJCNN, 2022)

Probabilistic modelling of general noisy multi-manifold data sets. - Marco Canducci; Peter Tiño; Michele Mastropietro (Artif. Intell., 2022)

1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments. - Marco Canducci; P. Awad; Abolfazl Taghribi; Mohammad Mohammadi 004; Michele Mastropietro; Sven De Rijcke; Reynier Peletier; Rory Smith; Kerstin Bunte; Peter Tiño (Astron. Comput., 2022)

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Michael Biehl; Wiebke Arlt; Peter Tino; Kerstin Bunte (CoRR, 2022)

ASAP - A sub-sampling approach for preserving topological structures modeled with geodesic topographic mapping. - Abolfazl Taghribi; Marco Canducci; Michele Mastropietro; Sven De Rijcke; Kerstin Bunte; Peter Tino (Neurocomputing, 2022)

Manifold Alignment Aware Ants: A Markovian Process for Manifold Extraction. - Mohammad Mohammadi 004; Peter Tino; Kerstin Bunte (Neural Comput., 2022)

Input-to-State Representation in Linear Reservoirs Dynamics. - Pietro Verzelli; Cesare Alippi; Lorenzo Livi; Peter Tino (IEEE Trans. Neural Networks Learn. Syst., 2022)

Population-Based Optimization on Riemannian Manifolds - Robert Simon Fong; Peter Tino (Springer, 2022)

An Approach for Dynamic Behavioural Prediction and Fault Injection in Cyber-Physical Systems. - Hayatullahi Bolaji Adeyemo; Rami Bahsoon; Peter Tino (BDCAT, 2023)

Inverse Solution Accuracy Using 12-Lead ECG vs 9 Significant Electrodes Derived by Greedy Algorithm. - Beata Ondrusova; Peter Tino; Jana Svehlíková (CinC, 2023)

Real-Time Workflow Scheduling in Cloud with Recursive Neural Network and List Scheduling. - Vahab Samandi; Peter Tino; Rami Bahsoon (HAIS, 2023)

Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold. - Fengzhen Tang; Peter Tino; Haibin Yu (IEEE Trans. Cybern., 2023)

Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data Streams. - Shuyi Zhang; Peter Tino; Xin Yao 001 (IEEE Trans. Knowl. Data Eng., 2023)

LAAT: Locally Aligned Ant Technique for Discovering Multiple Faint Low Dimensional Structures of Varying Density. - Abolfazl Taghribi; Kerstin Bunte; Rory Smith; Jihye Shin; Michele Mastropietro; Reynier F. Peletier; Peter Tino (IEEE Trans. Knowl. Data Eng., 2023)

An Interpretable Alternative to Neural Representation Learning for Rating Prediction - Transparent Latent Class Modeling of User Reviews. - Giuseppe Serra 002; Peter Tino; Zhao Xu 001; Xin Yao 001 (IJCNN, 2024)

Predictive Modeling in the Reservoir Kernel Motif Space. - Peter Tino; Robert Simon Fong; Roberto Fabio Leonarduzzi (IJCNN, 2024)

The Beauty of Prototype Based Learning. - Peter Tino (WSOM+, 2024)

Optimal electrode placements for localizing premature ventricular contractions using a single dipole cardiac source model. - Beata Ondrusova; Peter Tino; Jana Svehlíková (Comput. Biol. Medicine, 2024)

A distance function for stochastic matrices. - Antony Lee; Peter Tino; Iain Bruce Styles (CoRR, 2024)

Linear Simple Cycle Reservoirs at the edge of stability perform Fourier decomposition of the input driving signals. - Robert Simon Fong; Boyu Li; Peter Tino (CoRR, 2024)

Simple Cycle Reservoirs are Universal. - Boyu Li; Robert Simon Fong; Peter Tino (J. Mach. Learn. Res., 2024)

Universality of Real Minimal Complexity Reservoir. - Robert Simon Fong; Boyu Li; Peter Tino (AAAI, 2025)

More than a void? The detection and characterization of cavities in a simulated galaxy's interstellar medium. - Abolfazl Taghribi; Marco Canducci; Michele Mastropietro; Sven De Rijcke; Reynier Frans Peletier; Peter Tino; Kerstin Bunte (Astron. Comput., 2025)

Interpretable modelling and visualization of biomedical data. - Sreejita Ghosh; Elizabeth Sarah Baranowski; Michael Biehl; Wiebke Arlt; Peter Tiño; Kerstin Bunte (Neurocomputing, 2025)

On the importance of structural identifiability for machine learning with partially observed dynamical systems. - Janis Norden; Elisa Oostwal; Michael Chappell; Peter Tino; Kerstin Bunte (CoRR, 2025)

Whole-Genome Phenotype Prediction with Machine Learning: Open Problems in Bacterial Genomics. - Tamsin James; Ben Williamson; Peter Tino; Nicole Wheeler (CoRR, 2025)

Discriminative Subspace Emersion from learning feature relevances across different populations. - Marco Canducci; Lida Abdi; Alessandro Prete; Roland J. Veen; Michael Biehl; Wiebke Arlt; Peter Tino (CoRR, 2025)

Enzyme action optimizer: a novel bio-inspired optimization algorithm. - Ali Rodan; Abdel-Karim Al-Tamimi; Loai M. Alnemer; Seyedali Mirjalili; Peter Tino (J. Supercomput., 2025)