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Found 26 papers, 3 papers with code
Uncertainty estimation in spatial interpolation of satellite precipitation with ensemble learning
This demonstrates the potential of stacking to improve probabilistic predictions in spatial interpolation and beyond.
Learning using privileged information for segmenting tumors on digital mammograms
We repeat the student training procedure by providing the assistance of the teacher model this time.
Uncertainty estimation in satellite precipitation interpolation with machine learning
Compared to QR, LightGBM showed improved performance with respect to the quantile scoring rule by 11. 10%, followed by QRF (7. 96%), GRF (7. 44%), GBM (4. 64%) and QRNN (1. 73%).
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables.
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures.
Merging satellite and gauge-measured precipitation using LightGBM with an emphasis on extreme quantiles
To improve precipitation estimates, machine learning is applied to merge rain gauge-based measurements and gridded satellite precipitation products.
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale
Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature.
Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data
To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period.
Solar Power driven EV Charging Optimization with Deep Reinforcement Learning
Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future.
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques
Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes.
Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components.
Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery
In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials.
TraCon: A novel dataset for real-time traffic cones detection using deep learning
In this work, the YOLOv5 algorithm is employed, in order to find a solution for the efficient and fast detection of traffic cones.
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal.
Evaluating the Usefulness of Unsupervised monitoring in Cultural Heritage Monuments
In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images.
Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification
We hereby propose the Rank-R Feedforward Neural Network (FNN), a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters, thereby offering two core advantages compared to typical machine learning methods.
Creating a Baseline for Robust Energy Savings Estimation in Households
In this paper we provide an analytical methodology to handle such scenarios and create year-long hourly-based consumption baseline.
Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation
Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection.
Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications
A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem.
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently.
Common Mode Patterns for Supervised Tensor Subspace Learning
In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework.
Tensor-based Nonlinear Classifier for High-Order Data Analysis
We also introduce a new learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN on third-order hyperspectral data.
Tensor-Based Classifiers for Hyperspectral Data Analysis
Then, we introduce learning algorithms to train both the linear and the non-linear classifier in a way to i) to minimize the error over the training samples and ii) the weight coefficients satisfies the {\it rank}-1 canonical decomposition property.
Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery
Detection of moving objects in videos is a crucial step towards successful surveillance and monitoring applications.
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.
Variational Inference for Background Subtraction in Infrared Imagery
We propose a Gaussian mixture model for background subtraction in infrared imagery.
