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Found 9 papers, 2 papers with code
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.
Cooperation and interdependence in global science funding
Research and development investments are key to scientific and economic development and to the well-being of society.
Improved Naive Bayes with Mislabeled Data
Labeling mistakes are frequently encountered in real-world applications.
Diversity-Aware Meta Visual Prompting
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.
Towards Precise Flood Prediction via Hierachical Terrain Attention and Multi-Scale Rainfall Guidance
With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent.
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions.
Knowledge-Enhanced Relation Extraction Dataset
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches.
Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora
To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus.
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
