239 citations
- Vanderbilt UniversityUS3 papers
- Artificial Intelligence in Medicine (Canada)CA1 paper
- Barnard CollegeUS1 paper
- City University of Hong KongHK1 paper
- Columbia UniversityUS1 paper
- FoodlinkUS1 paper
- Fordham UniversityUS1 paper
- Immaterial Labs (United Kingdom)GB1 paper
- Johns Hopkins UniversityUS1 paper
- Marina Del Rey HospitalUS1 paper
- Nova Southeastern UniversityUS1 paper
- Predictive Biology (United States)US1 paper
7 papers
M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
Vsevolod Biryukov, Kamal Choudhary, Timur Bazhirov
The rapid advancement of artificial intelligence in materials science requires data standards and data management practices that can capture the complexity of real-world structures…
Hybrid Forecasting of Geopolitical Events
Daniel M. Benjamin, Fred Morstatter, Ali E. Abbas +26
Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we deve…
Pareto Multi-Task Learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li +2
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks…
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon +6
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically requir…
Fast and accessible first-principles calculations of vibrational properties of materials
Timur Bazhirov, E. X. Abot
We present example applications of an approach to first-principles calculations of vibrational properties of materials implemented within the Exabyte.io platform. We deploy models…
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Yuankai Huo, Zhoubing Xu, Shunxing Bao +3
A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging…