IEEE

Interpretable Deep Learning for Rice Leaf Disease Detection: A Self-Attention and XAI Framework

Abstract: Rice leaf diseases pose a significant challenge to food security and agricultural productivity. To address this challenge, innovative disease detection methods are required. A deep ...

Deep learning creates virtual multiplexed immunostaining to improve cancer diagnosis

Researchers at the University of California, Los Angeles (UCLA), in collaboration with pathologists from Hadassah Hebrew ...

Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
Tech Xplore on MSN

Decoding black box AI with human-readable data descriptions and influence

Artificial intelligence (AI), particularly deep learning models, are often considered black boxes because their ...
IEEE

An Interpretable SAR Image Filtering Algorithm

Abstract: Effective noise suppression is crucial for the subsequent interpretation tasks of synthetic aperture radar (SAR) imagery. Traditional SAR image processing techniques often overlook the ...
Morning Overview on MSN

Scientists build a ‘periodic table’ for AI models

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a ...
eLife

Separating selection from mutation in antibody language models

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...
GitHub

TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling" (ICLR 2025)

This is the official repository of the paper "TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling". It consists of two parts: One dot represents a performance score on one ...