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Found 6 papers, 0 papers with code
Truncated Cauchy Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers.
Generating Discriminative Object Proposals via Submodular Ranking
Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation.
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
Sparse representations have been successfully applied to signal processing, computer vision and machine learning.
Layer-Specific Adaptive Learning Rates for Deep Networks
In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points.
What makes an Image Iconic? A Fine-Grained Case Study
A natural approach to teaching a visual concept, e. g. a bird species, is to show relevant images.
Learning Structured Low-Rank Representations for Image Classification
An approach to learn a structured low-rank representation for image classification is presented.
