Abstract: Deep learning has been widely applied to seismic data interpolation. However, most existing methods are based on supervised learning, suffering from limitations such as low generalization ...

In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn meaningful image representations without labels, then ...

Starting September 1, 2025, the Bureau will begin receiving new and converted Electronic Surety Bonds (ESB) through NMLS for supervised lender licensees. All businesses with existing Maine supervised ...

Abstract: Annotated large-scale datasets are crucial for pathology image analysis, yet creating such datasets is challenging. Self-supervised learning (SSL) offers a potential solution to reduce the ...

1 German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany 2 Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany Introduction: Neurodegenerative ...

School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Ren Ji Hospital, School of ...

Most time series anomaly detection models aim to learn normal behavior from unlabelled data, identifying anomalies as deviations from this behavior. However, the lack of labelled data makes it ...

Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality and optimizes crop yield but also provides a sustainable ...

This repository contains the official implementation (in PyTorch) of the the paper SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model. SSAMBA is an advanced audio ...