335 citations · 1.2k across the 81 of their papers we have counts for
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Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection
F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu +2
Multiple Instance Learning (MIL) is a weakly supervised paradigm that has been successfully applied to many different scientific areas and is particularly well suited to medical im…
Examining the Benefits of Capsule Neural Networks
Arjun Punjabi, Jonas Schmid, Aggelos K. Katsaggelos
Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks. By replacing th…
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin +2
In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labell…
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Arjun Punjabi, Adam Martersteck, Yanran Wang +3
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, an…
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
Sara Bahaadini, Vahid Noroozi, Neda Rohani +3
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature…
Deep Multi-view Models for Glitch Classification
Sara Bahaadini, Neda Rohani, Scott Coughlin +3
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this…
Separation of time scales and direct computation of weights in deep neural networks
Nima Dehmamy, Neda Rohani, Aggelos Katsaggelos
Artificial intelligence is revolutionizing our lives at an ever increasing pace. At the heart of this revolution is the recent advancements in deep neural networks (DNN), learning…