%0 Conference Paper %1 magnussen2023utilizing %A Magnussen, Birk Martin %A Stern, Claudius %A Sick, Bernhard %B International Conference on Autonomic and Autonomous Systems (ICAS) %D 2023 %I ThinkMind %K imported itegpub isac-www MachineLearning NeuralNets ContinuousKernel IrregularlySampledData ReflectionSpectroscopy %P 49--53 %T Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features %U http://www.thinkmind.org/index.php?view=article&articleid=icas_2023_1_90_20043 %X Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.

@inproceedings{magnussen2023utilizing, abstract = {Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels to process irregularly sampled data are still desired. This article introduces a novel method utilizing continuous kernels for detecting global features at absolute positions in the data domain. Through a use case in processing multiple spatially resolved reflection spectroscopy data, which is sampled irregularly and inconsistently, we show that the proposed method is capable of processing such data natively without additional preprocessing as is needed using comparable methods. In addition, we show that the proposed method is able to achieve a higher prediction accuracy than a comparable network on a dataset with position-dependent features. Furthermore, a higher robustness to missing data compared to a benchmark network using data interpolation is observed, which allows the network to adapt to sensors with individual failed components without the need for retraining.}, added-at = {2023-03-28T15:25:31.000+0200}, author = {Magnussen, Birk Martin and Stern, Claudius and Sick, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/2269b5ec9da67ca65a245ffef833951de/ies}, booktitle = {International Conference on Autonomic and Autonomous Systems (ICAS)}, interhash = {8965e02c279a3ad19d3878f8b410a362}, intrahash = {269b5ec9da67ca65a245ffef833951de}, keywords = {imported itegpub isac-www MachineLearning NeuralNets ContinuousKernel IrregularlySampledData ReflectionSpectroscopy}, pages = {49--53}, publisher = {ThinkMind}, timestamp = {2023-03-28T15:25:31.000+0200}, title = {Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features}, url = {http://www.thinkmind.org/index.php?view=article&articleid=icas_2023_1_90_20043}, year = 2023 }