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Found 5 papers, 1 papers with code
Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning
Training deep neural networks is a challenging task.
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
We develop a Macroscopic Auxiliary Asymptotic-Preserving Neural Network (MA-APNN) method to solve the time-dependent linear radiative transfer equations (LRTEs), which have a multi-scale nature and high dimensionality.
Learning More Discriminative Local Descriptors for Few-shot Learning
Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled images and generalize over the new tasks.
A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs).
Big-Data Clustering: K-Means or K-Indicators?
The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering.
