[HTML][HTML] Soil Science-Informed Machine Learning
B Minasny, T Bandai, TA Ghezzehei, YC Huang, Y Ma… - Geoderma, 2024 - Elsevier
Abstract Machine learning (ML) applications in soil science have significantly increased over
the past two decades, reflecting a growing trend towards data-driven research addressing …
the past two decades, reflecting a growing trend towards data-driven research addressing …
PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data
Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging
the correlation between the auditory and visual modalities. Despite their many useful …
the correlation between the auditory and visual modalities. Despite their many useful …
Probabilistic deep learning to quantify uncertainty in air quality forecasting
A Murad, FA Kraemer, K Bach, G Taylor - Sensors, 2021 - mdpi.com
Data-driven forecasts of air quality have recently achieved more accurate short-term
predictions. However, despite their success, most of the current data-driven solutions lack …
predictions. However, despite their success, most of the current data-driven solutions lack …
White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
M Abou Ali, F Dornaika, I Arganda-Carreras - Algorithms, 2023 - mdpi.com
Deep learning (DL) has made significant advances in computer vision with the advent of
vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self …
vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self …
Optimal regularizations for data generation with probabilistic graphical models
A Fanthomme, F Rizzato, S Cocco… - Journal of Statistical …, 2022 - iopscience.iop.org
Understanding the role of regularization is a central question in statistical inference.
Empirically, well-chosen regularization schemes often dramatically improve the quality of the …
Empirically, well-chosen regularization schemes often dramatically improve the quality of the …
[PDF][PDF] Spectral, information-theoretic, and perturbative methods for quantum learning and error mitigation
E Peters - 2024 - uwspace.uwaterloo.ca
We present spectral and information-theoretic characterizations of learning tasks involving
quantum systems, and develop new perturbative error mitigation techniques for near-term …
quantum systems, and develop new perturbative error mitigation techniques for near-term …