32 citations · 56 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Rocket: Efficient and Scalable All-Pairs Computations on Heterogeneous Platforms
Stijn Heldens, Pieter Hijma, Ben van Werkhoven +3
All-pairs compute problems apply a user-defined function to each combination of two items of a given data set. Although these problems present an abundance of parallelism, data reu…
Auto-Tuning Dedispersion for Many-Core Accelerators
Alessio Sclocco, Henri E. Bal, Jason Hessels +2
In this paper, we study the parallelization of the dedispersion algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. An important contri…
Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators
Alessio Sclocco, Joeri van Leeuwen, Henri E. Bal +1
Dedispersion, the removal of deleterious smearing of impulsive signals by the interstellar matter, is one of the most intensive processing steps in any radio survey for pulsars and…