Feature Representation for Deep Neural Network Compression: Application to Compressive Sensing of Mammograms – As a major challenge in machine learning, a significant number of machine learning tasks use a low-dimensional representation of the data. It is hard to directly optimize the representation by training the deep network on a high-dimensional representation. In this paper, we propose a novel non-linear learning algorithm for model-based decision support for deep networks, wherein the high-dimensional representations of the data are optimized using a weighted least-squares loss to the loss function as well as a non-linear learning objective. Our algorithm is based on a simple yet effective regularization term which is efficient and practical, but requires no supervision for the deep network. The algorithm is applied to support the decision support task in which the input data of both data types for various decision contexts is shared (e.g. from the medical record, to users of healthcare services). In case of data sharing, it is also possible to compute weighted least-squares loss functions such that the data of different types are not shared by all models for a set of multiple decision contexts. We demonstrate the effectiveness of the proposed algorithm in two real and real-world scenarios.

We present a new neural network based framework for object segmentation with deep learning that combines convolutional and recurrent neural networks. The framework is fully unsupervised and can learn object segmentation with a small amount of supervision and trained a deep residual network with a small amount of supervision. We demonstrate the effectiveness of deep learning in object detection at scales ranging from tens of thousands to thousands of pixels for object segmentation. We show that the model can successfully segment objects with a low-dimensional manifold and can perform object detection well.

Multitask Learning for Knowledge Base Linking via Neural-Synthesis

BranchDNN: Boosting Machine Teaching with Multi-task Neural Machine Translation

Feature Representation for Deep Neural Network Compression: Application to Compressive Sensing of Mammograms

Deep Matching based Deep Convolutional Features for Semantic Segmentation

Deep Multimodal Convolutional Neural Networks for Object SearchWe present a new neural network based framework for object segmentation with deep learning that combines convolutional and recurrent neural networks. The framework is fully unsupervised and can learn object segmentation with a small amount of supervision and trained a deep residual network with a small amount of supervision. We demonstrate the effectiveness of deep learning in object detection at scales ranging from tens of thousands to thousands of pixels for object segmentation. We show that the model can successfully segment objects with a low-dimensional manifold and can perform object detection well.