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Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck
Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning.
Parametric Information Bottleneck to Optimize Stochastic Neural Networks
Here, we propose Parametric Information Bottleneck (PIB) for a neural network by utilizing (only) its model parameters explicitly to approximate the compression and the relevance.
Learning to Attend Relevant Regions in Videos from Eye Fixations
Attentively important regions in video frames account for a majority part of the semantics in each frame.
A novel approach to remove foreign objects from chest X-ray images
We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset.
Interpretation of smartphone-captured radiographs utilizing a deep learning-based approach
Recently, computer-aided diagnostic systems (CADs) that could automatically interpret medical images effectively have been the emerging subject of recent academic attention.
