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Found 4 papers, 1 papers with code
Diagnosing Ensemble Few-Shot Classifiers
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization
We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks.
Scale-Invariant Structure Saliency Selection for Fast Image Fusion
Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map.
