Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
A research team affiliated with UNIST has reported a new simulation tool to better understand how liquid-phase chemical ...
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 ...
Comparison of Deep-Learning Models for Classification of Cellular Phenotype From Flow Cytometry Data
Abstract: This study compares the relative utility of deep learning models as automated phenotypic classifiers, built with features of peripheral blood cell populations assayed with flow cytometry. We ...
Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
Abstract: Deep learning-based approaches have achieved remarkable success in various image-based dietary assessment applications, including food detection and estimating portion sizes. However, most ...
The challenge of efficiently detecting ripe and unripe strawberries in complex environments like greenhouses, marked by dense clusters of strawberries, frequent occlusions, overlaps, and fluctuating ...
Colorectal cancer (CRC) is the second most common cause of cancer-related death in the US. Screening reduces cancer mortality through early detection, but only 59% of eligible individuals are up to ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback