96 citations · 328 across the 30 of their papers we have counts for
32 papers
1-DREAM: 1D Recovery, Extraction and Analysis of Manifolds in noisy environments
Marco Canducci, Petra Awad, Abolfazl Taghribi +7
Filaments are ubiquitous in astronomical data sets. Be it in particle simulations or observations, filaments are always tracers of a perturbation in the equilibrium of the studied…
Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure
Petra Awad, Reynier Peletier, Marco Canducci +6
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the C…
Influence and Influenceability: Global Directionality in Directed Complex Networks
Niall Rodgers, Peter Tino, Samuel Johnson
Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many tradi…
Strong Connectivity in Real Directed Networks
Niall Rodgers, Peter Tino, Samuel Johnson
In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs…
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Sreejita Ghosh, Elizabeth S. Baranowski, Michael Biehl +3
Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the…
Network Hierarchy and Pattern Recovery in Directed Sparse Hopfield Networks
Niall Rodgers, Peter Tino, Samuel Johnson
Many real-world networks are directed, sparse and hierarchical, with a mixture of feed-forward and feedback connections with respect to the hierarchy. Moreover, a small number of '…
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
Fengzhen Tang, Haifeng Feng, Peter Tino +2
In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the…
A Geometric Framework for Pitch Estimation on Acoustic Musical Signals
Tom Goodman, Karoline van Gemst, Peter Tino
This paper presents a geometric approach to pitch estimation (PE)-an important problem in Music Information Retrieval (MIR), and a precursor to a variety of other problems in the f…
A Survey on Neural Network Interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis +1
Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning…
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
Abolfazl Taghribi, Kerstin Bunte, Rory Smith +4
Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performa…
Visualisation and knowledge discovery from interpretable models
Sreejita Ghosh, Peter Tino, Kerstin Bunte
Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness…
Input-to-State Representation in linear reservoirs dynamics
Pietro Verzelli, Cesare Alippi, Lorenzo Livi +1
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is…
A Framework for Population-Based Stochastic Optimization on Abstract Riemannian Manifolds
Robert Simon Fong, Peter Tino
We present Extended Riemannian Stochastic Derivative-Free Optimization (Extended RSDFO), a novel population-based stochastic optimization algorithm on Riemannian manifolds that add…
Dynamical Systems as Temporal Feature Spaces
Peter Tino
Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the blac…
Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
Giuseppe Longo, Erzsébet Merényi, Peter Tino
Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large…
Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers
Jade Powell, Simon Stevenson, Ilya Mandel +1
The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-popul…
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk +1
Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case,…
A mixture of experts model for predicting persistent weather patterns
Maria Perez-Ortiz, Pedro A. Gutierrez, Peter Tino +2
Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system w…
Feature Relevance Bounds for Ordinal Regression
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…
Linking Twitter Events With Stock Market Jitters
Fani Tsapeli, Nikolaos Bezirgiannidis, Peter Tino +1
Predicting investors reactions to financial and political news is important for the early detection of stock market jitters. Evidence from several recent studies suggests that onli…
Probabilistic Matching: Causal Inference under Measurement Errors
Fani Tsapeli, Peter Tino, Mirco Musolesi
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data…
Model-independent inference on compact-binary observations
Ilya Mandel, Will M. Farr, Andrea Colonna +3
The recent advanced LIGO detections of gravitational waves from merging binary black holes enhance the prospect of exploring binary evolution via gravitational-wave observations of…
A Classification Framework for Partially Observed Dynamical Systems
Yuan Shen, Peter Tino, Krasimira Tsaneva-Atanasova
We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using m…
Probabilistic classifiers with low rank indefinite kernels
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tino
Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval.…