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Found 5 papers, 4 papers with code
Predicting Economic Development using Geolocated Wikipedia Articles
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99. 0$\pm$0. 1%.
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Large amounts of labeled data are typically required to train deep learning models.
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.
