Peter Tiňo | DeepAI

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∙ 08/21/2023

Simple Cycle Reservoirs are Universal

Reservoir computation models form a subclass of recurrent neural network...

0 Boyu Li, et al. ∙

∙ 06/04/2022

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

Application of interpretable machine learning techniques on medical data...

30 Sreejita Ghosh, et al. ∙

∙ 02/01/2021

Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices

In this paper, we develop a new classification method for manifold-value...

0 Fengzhen Tang, et al. ∙

∙ 12/28/2020

A Survey on Neural Network Interpretability

Along with the great success of deep neural networks, there is also grow...

0 Yu Zhang, et al. ∙

∙ 12/08/2020

A Geometric Framework for Pitch Estimation on Acoustic Musical Signals

This paper presents a geometric approach to pitch estimation (PE)-an imp...

0 Tom Goodman, et al. ∙

∙ 09/17/2020

LAAT: Locally Aligned Ant Technique for detecting manifolds of varying density

Dimensionality reduction and clustering are often used as preliminary st...

10 Abolfazl Taghribi, et al. ∙

∙ 05/07/2020

Visualisation and knowledge discovery from interpretable models

Increasing number of sectors which affect human lives, are using Machine...

9 Sreejita Ghosh, et al. ∙

∙ 03/24/2020

Input representation in recurrent neural networks dynamics

Reservoir computing is a popular approach to design recurrent neural net...

10 Pietro Verzelli, et al. ∙

∙ 12/10/2019

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Advances in machine learning technologies have led to increasingly power...

0 Lukas Pfannschmidt, et al. ∙

∙ 07/15/2019

Dynamical Systems as Temporal Feature Spaces

Parameterized state space models in the form of recurrent networks are o...

9 Peter Tiňo, et al. ∙

∙ 03/24/2019

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

Data augmentation is rapidly gaining attention in machine learning. Synt...

0 Maria Perez-Ortiz, et al. ∙

∙ 03/24/2019

A mixture of experts model for predicting persistent weather patterns

Weather and atmospheric patterns are often persistent. The simplest weat...

0 Maria Perez-Ortiz, et al. ∙

∙ 02/20/2019

Feature Relevance Bounds for Ordinal Regression

The increasing occurrence of ordinal data, mainly sociodemographic, led ...

0 Lukas Pfannschmidt, et al. ∙

∙ 03/13/2017

Probabilistic Matching: Causal Inference under Measurement Errors

The abundance of data produced daily from large variety of sources has b...

0 Fani Tsapeli, et al. ∙

∙ 07/07/2016

A Classification Framework for Partially Observed Dynamical Systems

We present a general framework for classifying partially observed dynami...

0 Yuan Shen, et al. ∙

∙ 01/21/2016

Model-Coupled Autoencoder for Time Series Visualisation

We present an approach for the visualisation of a set of time series tha...

0 Nikolaos Gianniotis, et al. ∙

∙ 05/05/2015

Autoencoding Time Series for Visualisation

We present an algorithm for the visualisation of time series. To that en...

0 Nikolaos Gianniotis, et al. ∙

∙ 10/31/2012

Learning in the Model Space for Fault Diagnosis

The emergence of large scaled sensor networks facilitates the collection...

0 Huanhuan Chen, et al. ∙

∙ 11/09/2011

Scaling Up Estimation of Distribution Algorithms For Continuous Optimization

Since Estimation of Distribution Algorithms (EDA) were proposed, many at...

0 Weishan Dong, et al. ∙

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