25 citations · 66 across the 12 of their papers we have counts for
17 papers
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…
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…
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…