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Deeply-Supervised Nets
TLDR
Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent. Expand
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Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
TLDR
We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. Expand
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Multi-view kernel construction
TLDR
In many problem domains data may come from multiple sources (or views), such as video and audio from a camera or text on and links to a web page. Expand
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What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks
TLDR
Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks. Expand
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Optimizing Safety of Selective Internal Radiation Therapy (SIRT) of Hepatic Tumors with 90Y Resin Microspheres: A Systematic Approach to Preparation and Radiometric Procedures
Arterial administration of 90Y microspheres is used for salvage therapy in patients with primary or metastasized tumors within the liver. The clinical use of high-yield beta emitters presents uniqueExpand
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Bioinspired decision architectures containing host and microbiome processing units.
Biomimetic robots have been used to explore and explain natural phenomena ranging from the coordination of ants to the locomotion of lizards. Here, we developed a series of decision architecturesExpand
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Operator Theory for Analysis of Convex Optimization Methods in Machine Learning
Author(s): Gallagher, Patrick W. | Abstract: As machine learning has more closely interacted with optimization, the concept of convexity has loomed large. Two properties beyond simple convexity haveExpand
Pseudocontractive Updates: A Common Thread in Convex Optimization Methods
Many convex optimization methods are conceived of and analyzed in a largely separate fashion. In contrast to this traditional separation, this manuscript points out and demonstrates the utility of anExpand
Properties of Pseudocontractive Updates in Convex Optimization
Many convex optimization methods are conceived of and analyzed in a largely separate fashion. In contrast to this traditional separation, this manuscript points out and demonstrates the utility of anExpand