A research team leverages chlorophyll fluorescence (ChlF) imaging, a cutting-edge technique, to identify reliable pre-symptomatic diagnostic indicators for rice blast and brown spot.

Ground-breaking, GPU-free machine learning now installs in minutes on Ubuntu, Red Hat Enterprise Linux, and Fedora, complete ...

Recently, Hongwei Li’s team from China University of Geosciences published a research article titled “scSCC: A swapped contrastive learning-based clustering method for single-cell gene ...

By introducing a novel top-k queue-based contrastive learning strategy, the framework significantly improves the model’s accuracy in distinguishing challenging positive and negative samples and its ...

Keywords: contrastive learning, self-supervised learning, neurodegenerative disorders, deep learning, structural magnetic resonance imaging, Alzheimer's disease, frontotemporal lobar degeneration ...

Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning ...

Keywords: active learning, contrastive learning, clustering, semi-supervised learning, human-in-the-loop Citation: Roda H and Geva AB (2024) Semi-supervised active learning using convolutional ...

First, to bridge the gap when applying contrastive learning to intrusion detection data, we proposed a heuristic method to build contrastive tasks based on random masking of network packet sequences, ...