31 citations · 151 across the 20 of their papers we have counts for
11 papers · 1 filter
Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study
Luis Pedro Silvestrin, Shujian Yu, Mark Hoogendoorn
Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this ar…
Multivariate Time Series Early Classification Across Channel and Time Dimensions
Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn +1
Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent. Moreover, there is a growing populari…
Disentangled (Un)Controllable Features
Jacob E. Kooi, Mark Hoogendoorn, Vincent François-Lavet
In the context of MDPs with high-dimensional states, downstream tasks are predominantly applied on a compressed, low-dimensional representation of the original input space. A varie…
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensions
Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn +1
In current research, machine and deep learning solutions for the classification of temporal data are shifting from single-channel datasets (univariate) to problems with multiple ch…
Towards a General Purpose CNN for Long Range Dependencies in D
David W. Romero, David M. Knigge, Albert Gu +4
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learn…
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS
Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn +1
Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly releva…
The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models
Etienne van de Bijl, Jan Klein, Joris Pries +3
Novel prediction methods should always be compared to a baseline to know how well they perform. Without this frame of reference, the performance score of a model is basically meani…
A Framework for Imbalanced Time-series Forecasting
Luis P. Silvestrin, Leonardos Pantiskas, Mark Hoogendoorn
Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic…
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers +2
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizo…
Mixing Consistent Deep Clustering
Daniel Lutscher, Ali el Hassouni, Maarten Stol +1
Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature r…
Reinforcement Learning for Personalized Dialogue Management
Floris den Hengst, Mark Hoogendoorn, Frank van Harmelen +1
Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learn…