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[HTML][HTML] Ten deep learning techniques to address small data problems with remote sensing
Researchers and engineers have increasingly used Deep Learning (DL) for a variety of
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …
Remote Sensing (RS) tasks. However, data from local observations or via ground truth is …
Auditing large language models: a three-layered approach
Large language models (LLMs) represent a major advance in artificial intelligence (AI)
research. However, the widespread use of LLMs is also coupled with significant ethical and …
research. However, the widespread use of LLMs is also coupled with significant ethical and …
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …
machine learning (ML) methodologies to medical data to extract value from them and …
The importance of being external. methodological insights for the external validation of machine learning models in medicine
Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
Targeted validation: validating clinical prediction models in their intended population and setting
Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …
validation studies are sometimes carefully designed to match an intended population/setting …
[HTML][HTML] Comparing handcrafted features and deep neural representations for domain generalization in human activity recognition
Human Activity Recognition (HAR) has been studied extensively, yet current approaches are
not capable of generalizing across different domains (ie, subjects, devices, or datasets) with …
not capable of generalizing across different domains (ie, subjects, devices, or datasets) with …
Multiscale data-driven seismic full-waveform inversion with field data study
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-
resolution subsurface models from seismograms, is a powerful imaging technique in …
resolution subsurface models from seismograms, is a powerful imaging technique in …
[HTML][HTML] The challenges of using machine learning models in psychiatric research and clinical practice
To understand the complex nature of heterogeneous psychiatric disorders, scientists and
clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging …
clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging …
[HTML][HTML] Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning
Advances in computer vision, driven by deep learning, allows for the inference of
environmental pollution and its potential sources from images. The spatial and temporal …
environmental pollution and its potential sources from images. The spatial and temporal …
Statistics in the service of science: Don't let the tail wag the dog
Statistical modeling is generally meant to describe patterns in data in service of the broader
scientific goal of develo** theories to explain those patterns. Statistical models support …
scientific goal of develo** theories to explain those patterns. Statistical models support …