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Found 6 papers, 1 papers with code
Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests
Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning.
Estimating the treatment effect over time under general interference through deep learner integrated TMLE
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to their assumption of independent individuals.
On the Convergence of Sigmoid and tanh Fuzzy General Grey Cognitive Maps
By comparing with the convergence theorems of FCM and FGCM, the effectiveness of the theorems proposed in this paper was verified.
Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation
Accordingly, this study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Deep Inferential Spatial-Temporal Network for Forecasting Air Pollution Concentrations
Air pollution poses a serious threat to human health as well as economic development around the world.
