Lawrence Carin
Duke University
964 Papers
8.2K Citations
Lawrence Carin is an academic researcher from Duke University. The author has contributed to research in topics: Computer science & Hidden Markov model. The author has an hindex of 84, co-authored 949 publications. Previous affiliations of Lawrence Carin include Indian Institute of Technology Kanpur & University of Maryland, College Park.
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Papers
Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.
TL;DR: The JCFO is introduced, a novel algorithm that uses a sparse Bayesian approach to jointly identify both the optimal nonlinear classifier for diagnosis and the optimal set of genes on which to base that diagnosis.
Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images
TL;DR: This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning, and enables threat recognition based on examples from the labeled data, and can reduce false alarm rates by matching the statistics on the hand-collected backgrounds to that of the real world data.
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.
Rachel Lea Draelos,David Dov,Maciej A. Mazurowski,Joseph Y. Lo,Ricardo Henao,Geoffrey D. Rubin,Lawrence Carin +6 more
TL;DR: In this paper, a rule-based method was developed for automatically extracting abnormality labels from free-text radiology reports with an average F-score of 0.976 (min 0.541, max 1.0).
Continuing progress of spike sorting in the era of big data.
David E. Carlson,Lawrence Carin +1 more
TL;DR: Recent efforts to attack spike sorting challenges have primarily focused on increasing accuracy and reliability while being computationally scalable, and adding additional stages to the data processing pipeline and using divide-and-conquer algorithmic approaches.
•Posted Content
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Dinghan Shen,Qinliang Su,Paidamoyo Chapfuwa,Wenlin Wang,Guoyin Wang,Lawrence Carin,Ricardo Henao +6 more
TL;DR: An end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables and a neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function.