Latest AI and machine learning research in medicare for healthcare professionals.
Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings. Yet existing data science benchmarks fall short due to fragmented evaluation interfaces that make cross-benchmark comparison difficult, narrow task coverage and a lack of rigorous data grounding. In particular, we show that a substantial portion of tasks in current benc...
Robust machine learning for regulatory genomics is studied under biologically and technically induced distribution shifts. Deep convolutional and attention based models achieve strong in distribution performance on DNA regulatory sequence prediction tasks but are usually evaluated under i.i.d. assumptions, even though real applications involve cell type specific programs, evolutionary turnover, as...
1Reconstructing genomes from metagenomic assemblies is foundational to microbiome research, yet metagenome binning remains constrained by a persistent...
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a res...
UNLABELLED: This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) m...
Neural networks' insufficient interpretability can lead to unguaranteed Safety of the Intended Functionality (SOTIF) issues when perceptual results ar...
We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We ...
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilit...
Instruction tuning has become a foundation for unlocking the capabilities of large-scale pretrained models and improving their performance on comple...
Many real-world classification problems, such as plant identification, have extremely long-tailed class distributions. In order for prediction sets ...
Unlike classification, whose goal is to estimate the class of each data point in a dataset, prevalence estimation or quantification is a task that a...
Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence o...
Diffusion policy has demonstrated promising performance in the field of robotic manipulation. However, its effectiveness has been primarily limited ...
In the context of Extreme Multi-label Text Classification (XMTC), where labels are assigned to text instances from a large label space, the long-tai...
Background: The use of Electronic Health Records (EHRs) for epidemiological studies and artificial intelligence (AI) training is increasing rapidly....
Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navig...
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative traject...
Deep learning methods have significantly advanced the development of intelligent rinterpretation in remote sensing (RS), with foundational model res...
Deep learning has advanced mass spectrometry data interpretation, yet most models remain feature extractors rather than unified scoring frameworks. ...
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent p...