Peter Tiño

发表

Probabilistic Classification Vector Machine at large scale

Peter Tiño, Frank-Michael Schleif, Andrej Gisbrecht, 2015, ESANN.

Support Vector Ordinal Regression using Privileged Information

Pedro Antonio Gutiérrez, Huanhuan Chen, Fengzhen Tang, 2014, ESANN.

Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness

Peter Tiño, Rami Bahsoon, Peter R. Lewis, 2015, 2015 IEEE International Conference on Web Services.

Ordinal-based metric learning for learning using privileged information

Peter Tiño, Shereen Fouad, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Probabilistic Classification Vector Machines

Huanhuan Chen, Peter Tiño, Xin Yao, 2009, IEEE Transactions on Neural Networks.

Does Money Matter? An Artificial Intelligence Approach

Peter Tiño, Jonathan A. Tepper, Graham Kendall, 2006, JCIS.

The profitability of trading volatility using real-valued and symbolic models

Peter Tiño, Georg Dorffner, Christian Schittenkopf, 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

Building predictive models on complex symbolic sequences with a second-order recurrent BCM network with lateral inhibition

Peter Tiño, Michal Stancík, Lubica Benusková, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A Principled Approach to the Analysis of Process Mining Algorithms

Peter Tiño, Phil Weber, Behzad Bordbar, 2011, IDEAL.

Short term memory in input-driven linear dynamical systems

Peter Tiño, Ali Rodan, 2013, Neurocomputing.

Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space

Huanhuan Chen, Peter Tiño, Xin Yao, 2014, Comput. Chem. Eng..

Architectural Bias in Recurrent Neural Networks - Fractal Analysis

Peter Tiño, Barbara Hammer, 2002, ICANN.

A principled approach to mining from noisy logs using Heuristics Miner

Peter Tiño, Phil Weber, Behzad Bordbar, 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Learning long-term dependencies in NARX recurrent neural networks

Peter Tiño, C. Lee Giles, Tsungnan Lin, 1996, IEEE Trans. Neural Networks.

Improving Sampling in Evolution Strategies Through Mixture-Based Distributions Built from Past Problem Instances

Peter Tiño, Bernhard Sendhoff, Stefan Menzel, 2020, PPSN.

Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks)

Peter Tiño, C. Lee Giles, Bill G. Horne, 2001, Neural Computation.

Oversampling the Minority Class in the Feature Space

Pedro Antonio Gutiérrez, César Hervás-Martínez, Peter Tiño, 2016, IEEE Transactions on Neural Networks and Learning Systems.

A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses

Peter Tiño, Juan C. Cuevas-Tello, Somak Raychaudhury, 2006, ECML.

Managing Diversity in Regression Ensembles

Peter Tiño, Gavin Brown, Jeremy L. Wyatt, 2005, J. Mach. Learn. Res..

Dynamics and Topographic Organization of Recursive Self-Organizing Maps

Peter Tiño, Igor Farkas, Jort van Mourik, 2006, Neural Computation.

Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way

Peter Tiño, Ian T. Nabney, I. Nabney, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Comparison of Echo State Networks with Simple Recurrent Networks and Variable-Length Markov Models on Symbolic Sequences

Peter Tiño, Michal Cernanský, 2007, ICANN.

Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences

Peter Tiño, Miroslav Koteles, P. Tiňo, 1999, IEEE Trans. Neural Networks.

Efficient Probabilistic Classification Vector Machine With Incremental Basis Function Selection

Huanhuan Chen, Peter Tiño, Xin Yao, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Real-Time Detection of Process Change using Process Mining

Peter Tiño, Phil Weber, Behzad Bordbar, 2011, ICCSW.

Learning and Extracting Initial Mealy Automata with a Modular Neural Network Model

Peter Tiño, Jozef Sajda, P. Tiňo, 1995, Neural Comput..

A Spatial Mixture Approach to Inferring Sub-ROI Spatio-temporal Patterns from Rapid Event-Related fMRI Data

Stephen D. Mayhew, Peter Tiño, Yuan Shen, 2013, MICCAI.

Automated Detection of Galaxy Groups Through Probabilistic Hough Transform

Peter Tiño, Arif Babul, Richard J. Pearson, 2015, ICONIP.

On Conditions for Intermittent Search in Self-organizing Neural Networks

Peter Tiño, 2007, MICAI.

Visualization of Tree-Structured Data Through Generative Topographic Mapping

Peter Tiño, Nikolaos Gianniotis, P. Tiňo, 2008, IEEE Transactions on Neural Networks.

Large Scale Indefinite Kernel Fisher Discriminant

Peter Tiño, Frank-Michael Schleif, Andrej Gisbrecht, 2015, SIMBAD.

Sparsification of Indefinite Learning Models

Peter Tiño, Frank-Michael Schleif, Christoph Raab, 2018, S+SSPR.

Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network

Huanhuan Chen, Peter Tiño, Xin Yao, 2014, Eng. Appl. Artif. Intell..

Measuring Generalization Performance in Coevolutionary Learning

Peter Tiño, Xin Yao, Siang Yew Chong, 2008, IEEE Transactions on Evolutionary Computation.

Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised Multiagent Strategies

Peter Tiño, Richard Price, P. Tiňo, 2004, PPSN.

Model Metric Co-Learning for Time Series Classification

Huanhuan Chen, Fengzhen Tang, Peter Tiño, 2015, IJCAI.

Prototype Based Modelling for Ordinal Classification

Peter Tiño, Shereen Fouad, P. Tiňo, 2012, IDEAL.

Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection Manifolds

Peter Tiño, Yi Sun, Ian T. Nabney, 2001, ICANN.

Graded Grammaticality in Prediction Fractal Machines

Peter Tiño, Georg Dorffner, Shan Parfitt, 1999, NIPS.

Recent trends in learning of structured and non-standard data

Thomas Villmann, Peter Tiño, Frank-Michael Schleif, 2014, ESANN.

Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks

Peter Tiño, 2006, PPSN.

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Peter Tiño, Orla M. Doyle, Paul A. Tiffin, 2013, IEEE Transactions on Biomedical Engineering.

Exploitation of Pairwise Class Distances for Ordinal Classification

Pedro Antonio Gutiérrez, César Hervás-Martínez, Peter Tiño, 2013, Neural Computation.

Architectural Bias in Recurrent Neural Networks: Fractal Analysis

Peter Tiño, Barbara Hammer, P. Tiňo, 2002, Neural Computation.

Minimum Complexity Echo State Network

Peter Tiño, Ali Rodan, P. Tiňo, 2011, IEEE Transactions on Neural Networks.

Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model

Peter Tiño, Xiaoxia Wang, Mark A. Fardal, 2008, ECML/PKDD.

Learning in the Model Space for Cognitive Fault Diagnosis

Huanhuan Chen, Peter Tiño, Xin Yao, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Equilibria of Iterative Softmax and Critical Temperatures for Intermittent Search in Self-Organizing Neural Networks

Peter Tiño, 2007, Neural Computation.

Adaptive Metric Learning Vector Quantization for Ordinal Classification

Peter Tiño, Shereen Fouad, P. Tiňo, 2012, Neural Computation.

One-shot Learning of Poisson Distributions in fast changing environments

Peter Tiño, 2010, Learning paradigms in dynamic environments.

Markovian Bias of Neural-based Architectures With Feedback Connections

Peter Tiño, Mikael Bodén, Barbara Hammer, 2007, Perspectives of Neural-Symbolic Integration.

Relationship Between Generalization and Diversity in Coevolutionary Learning

Peter Tiño, Xin Yao, Siang Yew Chong, 2009, IEEE Transactions on Computational Intelligence and AI in Games.

Dimensionality reduction and topographic mapping of binary tensors

Hong Yan, Peter Tiño, Mikael Bodén, 2013, Pattern Analysis and Applications.

Ordinal regression neural networks based on concentric hyperspheres

Pedro Antonio Gutiérrez, César Hervás-Martínez, Peter Tiño, 2014, Neural Networks.

Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data - How Close Can We Get?

Peter Tiño, 2013, Entropy.

A Survey on Neural Network Interpretability pdf

Peter Tiño, Ales Leonardis, Ke Tang, 2020, ArXiv.

Spatial–temporal modelling of fMRI data through spatially regularized mixture of hidden process models

Stephen D. Mayhew, Peter Tiño, Yuan Shen, 2014, NeuroImage.

Simple Deterministically Constructed Recurrent Neural Networks

Peter Tiño, Ali Rodan, 2010, IDEAL.

Learning the deterministically constructed Echo State Networks

Huanhuan Chen, Fengzhen Tang, Peter Tiño, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Predictive Modeling with Echo State Networks

Peter Tiño, Michal Cernanský, 2008, ICANN.

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns

Peter Tiño, Gabriela Kosková, 2004, Neural Networks.

On Reliability Of Simulations Of Complex Co-Evolutionary Processes

Peter Tiño, Xin Yao, Siang Yew Chong, 2010, ECMS.

Multilinear Decomposition and Topographic Mapping of Binary Tensors

Hong Yan, Peter Tiño, Mikael Bodén, 2010, ICANN.

Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons

Peter Tiño, Ashley J. S. Mills, P. Tiňo, 2005, Neural Computation.

A generative probabilistic approach to visualizing sets of symbolic sequences

Peter Tiño, Yi Sun, Ata Kabán, 2004, KDD '04.

Introducing a Star Topology into Latent Class Models for Collaborative Filtering

Peter Tiño, Gabriela Kosková, 2004, AIAI.

Financial volatility trading using recurrent neural networks

Peter Tiño, Georg Dorffner, Christian Schittenkopf, 2001, IEEE Trans. Neural Networks.

Learning in the Model Space for Fault Diagnosis pdf

Huanhuan Chen, Peter Tiño, Xin Yao, 2012, ArXiv.

Fisher memory of linear Wigner echo state networks

Peter Tiño, 2017, ESANN.

Self-Awareness for Dynamic Knowledge Management in Self-Adaptive Volunteer Services

Peter Tiño, Rami Bahsoon, Abdessalam Elhabbash, 2017, 2017 IEEE International Conference on Web Services (ICWS).

Autoencoding time series for visualisation pdf

Peter Tiño, Kai Lars Polsterer, Nikolaos Gianniotis, 2015, ESANN.

Short Term Memory Quantifications in Input-Driven Linear Dynamical Systems

Peter Tiño, Ali Rodan, 2012, ESANN.

Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays

Peter Tiño, P. Tiňo, 2009, BMC Bioinformatics.

Nonlinear Prediction of Quantitative Structure-Activity Relationships

Peter Tiño, Yi Sun, Ian T. Nabney, 2004, J. Chem. Inf. Model..

Learning long-term dependencies is not as difficult with NARX networks

Peter Tiño, C. Lee Giles, Tsungnan Lin, 1995, NIPS.

Negatively Correlated Echo State Networks

Peter Tiño, Ali Rodan, P. Tiňo, 2011, ESANN.

Machine learning and data analysis in astroinformatics

Peter Tiño, Michael Biehl, Giuseppe Longo, 2018, ESANN.

Concept drift detection for online class imbalance learning

Peter Tiño, Xin Yao, Leandro L. Minku, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Classification of sparsely and irregularly sampled time series: A learning in model space approach

Peter Tiño, Yuan Shen, Krasimira Tsaneva-Atanasova, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Asymptotic Fisher memory of randomized linear symmetric Echo State Networks

Peter Tiño, P. Tiňo, 2018, Neurocomputing.

Novel approaches in machine learning and computational intelligence

Peter Tiño, Alessio Micheli, Frank-Michael Schleif, 2013, Neurocomputing.

Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps

Peter Tiño, Ali Rodan, P. Tiňo, 2012, Neural Computation.

Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data

Peter Tiño, Yuan Shen, Nahed Alowadi, 2016, WSOM.

Incremental probabilistic classification vector machine with linear costs

Peter Tiño, Frank-Michael Schleif, H. Chen, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons

Peter Tiño, Ashley J. S. Mills, 2005, ICNC.

Probabilistic classifiers with low rank indefinite kernels pdf

Peter Tiño, Frank-Michael Schleif, Andrej Gisbrecht, 2016, ArXiv.

Indefinite Core Vector Machine

Peter Tiño, Frank-Michael Schleif, P. Tiňo, 2017, Pattern Recognit..

Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics

Peter Tiño, Georg Dorffner, Christian Schittenkopf, 1998, Hybrid Neural Systems.

On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map

Peter Tiño, Igor Farkas, 2005, ICNC.

A Utility Model for Volunteered Service Composition

Peter Tiño, Rami Bahsoon, Peter R. Lewis, 2014, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing.

Time-Dependent Series Variance Estimation via Recurrent Neural Networks

Peter Tiño, Nikolay I. Nikolaev, Evgueni N. Smirnov, 2011, ICANN.

Ordinal regression based on learning vector quantization

Fengzhen Tang, Peter Tiño, P. Tiňo, 2017, Neural Networks.

How accurate are the time delay estimates in gravitational lensing? pdf

Peter Tiño, Juan C. Cuevas-Tello, Somak Raychaudhury, 2006, ArXiv.

Extended stochastic derivative-free optimization on riemannian manifolds

Peter Tiño, Robert Simon Fong, P. Tiňo, 2019, GECCO.

Estimating Time Delay in Gravitationally Lensed Fluxes

Peter Tiño, Juan C. Cuevas-Tello, Somak Raychaudhury, 2009, Similarity-based learning on structures.

Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

Peter Tiño, Xin Yao, Tianshi Chen, 2011, IEEE Transactions on Evolutionary Computation.

Recursive Self-organizing Map as a Contractive Iterative Function System

Peter Tiño, Igor Farkas, Jort van Mourik, 2005, IDEAL.

Visualization of Structured Data via Generative Probabilistic Modeling

Peter Tiño, Nikolaos Gianniotis, 2009, Similarity-Based Clustering.

Probabilistic matching: Causal inference under measurement errors

Peter Tiño, Mirco Musolesi, Fani Tsapeli, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Complex Coevolutionary Dynamics—Structural Stability and Finite Population Effects

Peter Tiño, Xin Yao, Siang Yew Chong, 2013, IEEE Transactions on Evolutionary Computation.

Visualisation of tree-structured data through generative probabilistic modelling

Peter Tiño, Nikolaos Gianniotis, 2007, ESANN.

Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation

Peter Tiño, Frank-Michael Schleif, Ata Kabán, 2016, ANNPR.

Linking Twitter Events With Stock Market Jitters pdf

Peter Tiño, Mirco Musolesi, Nikolaos Bezirgiannidis, 2017, ArXiv.

Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders

Peter Tiño, Michael Biehl, Gert-Jan de Vries, 2017, ESANN.

Predicting the Future of Discrete Sequences from Fractal Representations of the Past

Peter Tiño, Georg Dorffner, P. Tiňo, 2001, Machine Learning.

Feature relevance bounds for ordinal regression pdf

Peter Tiño, Michael Biehl, Barbara Hammer, 2019, ESANN.

Towards Self-Aware Service Composition

Peter Tiño, Rami Bahsoon, Abdessalam Elhabbash, 2014, 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS).

Recurrent Neural Networks with Iterated Function Systems Dynamics

Peter Tiño, Georg Dorffner, P. Tiňo, 1998, NC.

Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks

Peter Tiño, 2007, ICONIP.

Learning in indefinite proximity spaces - recent trends

Peter Tiño, Frank-Michael Schleif, Yingyu Liang, 2016, ESANN.

Process Mining in Non-Stationary Environments

Peter Tiño, Phil Weber, Behzad Bordbar, 2012, ESANN.

Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform

Hong Yan, Peter Tiño, Hongya Zhao, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model

Peter Tiño, Nikolaos Gianniotis, Somak Raychaudhury, 2009, ICANN.

A mixture of experts model for predicting persistent weather patterns

María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tiño, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets pdf

María Pérez-Ortiz, César Hervás-Martínez, Peter Tiño, 2019, AAAI.

Randomized Recurrent Neural Networks

Peter Tiño, Claudio Gallicchio, Alessio Micheli, 2018, ESANN.

Degree distribution and scaling in the Connecting Nearest Neighbors model

Peter Tiño, Mária Markosová, Boris Rudolf, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Markovian architectural bias of recurrent neural networks

Peter Tiño, Lubica Benusková, Michal Cernanský, 2004, IEEE Transactions on Neural Networks.

Theory of Input Driven Dynamical Systems

Peter Tiño, Herbert Jaeger, Manjunath Gandhi, 2012, ESANN.

Computational Intelligence in Astronomy - A Win-Win Situation

Peter Tiño, Somak Raychaudhury, P. Tiňo, 2012, TPNC.

Incorporating Privileged Information Through Metric Learning

Peter Tiño, Shereen Fouad, Petra Schneider, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Probabilistic Modelling for Delay Estimation in Gravitationally Lensed Photon Streams

Peter Tiño, Sultanah Al Otaibi, Somak Raychaudhury, 2016, IDEAL.

Fast parzen window density estimator

Peter Tiño, Xiaoxia Wang, Arif Babul, 2009, 2009 International Joint Conference on Neural Networks.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

Peter Tiño, Yuan Shen, Shereen Fouad, 2016, Front. Comput. Neurosci..

Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling

Peter Tiño, Nikolaos Gianniotis, 2007, IJCAI.

Extracting stochastic machines from recurrent neural networks trained on complex symbolic sequences

Peter Tiño, V. Vojtek, P. Tiňo, 1997, Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97.

Recurrent Neural Networks with Small Weights Implement Definite Memory Machines

Peter Tiño, Barbara Hammer, P. Tiňo, 2003, Neural Computation.

A Probabilistic Ensemble Pruning Algorithm

Huanhuan Chen, Peter Tiño, Xin Yao, 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

Linear dynamical based models for sequential domains

Peter Tiño, Alessandro Sperduti, Luca Pasa, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

The Benefits of Modeling Slack Variables in SVMs

Pedro Antonio Gutiérrez, Huanhuan Chen, Fengzhen Tang, 2015, Neural Computation.

Adapting to NAT timeout values in P2P overlay networks

Peter Tiño, Richard Price, 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

Time-dependent series variance learning with recurrent mixture density networks

Peter Tiño, Nikolay I. Nikolaev, Evgueni N. Smirnov, 2013, Neurocomputing.

Semisupervised learning of hierarchical latent trait models for data visualization

Peter Tiño, Yi Sun, Ata Kabán, 2005, IEEE Transactions on Knowledge and Data Engineering.

Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series

Peter Tiño, Georg Dorffner, Christian Schittenkopf, 2001, Pattern Analysis & Applications.

Indefinite Proximity Learning: A Review

Peter Tiño, Frank-Michael Schleif, P. Tiňo, 2015, Neural Computation.

Using Dimensionality Reduction Method for Binary Data to Questionnaire Analysis

Peter Tiño, Jakub Mazgut, Martina Paulinyová, 2011, MEMICS.

Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application

Peter Tiño, Markus Harva, Xin Yao, 2009, Pattern Recognit..

A General Framework for a Principled Hierarchical Visualization of Multivariate Data

Peter Tiño, Ata Kabán, Mark A. Girolami, 2002, IDEAL.

A Framework for the Analysis of Process Mining Algorithms

Peter Tiño, Phil Weber, Behzad Bordbar, 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Predictive Ensemble Pruning by Expectation Propagation

Huanhuan Chen, Peter Tiño, Xin Yao, 2009, IEEE Transactions on Knowledge and Data Engineering.

One-Shot Learning of Poisson Distributions in Serial Analysis of Gene Expression

Peter Tiño, 2011, ISNN.

Learning Using Privileged Information in Prototype Based Models

Peter Tiño, Shereen Fouad, Petra Schneider, 2012, ICANN.

Artificial Neural Network Models

Peter Tiño, Alessandro Sperduti, Lubica Benusková, 2015, Handbook of Computational Intelligence.

Building Predictive Models from Fractal Representations of Symbolic Sequences

Peter Tiño, Georg Dorffner, 1999, NIPS.

Spatial representation of symbolic sequences through iterative function systems

Peter Tiño, P. Tiňo, 1999, IEEE Trans. Syst. Man Cybern. Part A.

Supervised low rank indefinite kernel approximation using minimum enclosing balls

Peter Tiño, Frank-Michael Schleif, Andrej Gisbrecht, 2018, Neurocomputing.

Interaction-Awareness for Self-Adaptive Volunteer Computing

Peter Tiño, Rami Bahsoon, Abdessalam Elhabbash, 2016, 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).

Interpreting Node Embedding with Text-labeled Graphs

Peter Tiño, Zhao Xu, Mathias Niepert, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization

Peter Tiño, Bernhard Sendhoff, Stefan Menzel, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Model-coupled autoencoder for time series visualisation

Peter Tiño, Kai Lars Polsterer, Nikolaos Gianniotis, 2016, Neurocomputing.