Search Results for author:
Found 15 papers, 3 papers with code
Autoencoding Time Series for Visualisation
We present an algorithm for the visualisation of time series.
Probabilistic classifiers with low rank indefinite kernels
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.
A Classification Framework for Partially Observed Dynamical Systems
We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space.
Probabilistic Matching: Causal Inference under Measurement Errors
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.
Feature Relevance Bounds for Ordinal Regression
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Data augmentation is rapidly gaining attention in machine learning.
A mixture of experts model for predicting persistent weather patterns
Weather and atmospheric patterns are often persistent.
Dynamical Systems as Temporal Feature Spaces
We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.
Input-to-State Representation in linear reservoirs dynamics
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance.
Visualisation and knowledge discovery from interpretable models
In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
A Geometric Framework for Pitch Estimation on Acoustic Musical Signals
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 field.
Information Retrieval
Music Information Retrieval
Sound
Computational Geometry
Audio and Speech Processing
68R01
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization.
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques.
