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Andrew Rabinovich
Researcher at Google
Publications - 67
Citations - 51886
Andrew Rabinovich is an academic researcher from Google. The author has contributed to research in topics: Convolutional neural network & Artificial neural network. The author has an hindex of 28, co-authored 67 publications receiving 37872 citations. Previous affiliations of Andrew Rabinovich include University of California, San Diego & Discovery Institute.
Papers
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Posted Content
Going Deeper with Convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: A deep convolutional neural network architecture codenamed Inception is proposed that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Posted Content
ParseNet: Looking Wider to See Better
TL;DR: This work presents a technique for adding global context to deep convolutional networks for semantic segmentation, and achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines.
Proceedings ArticleDOI
SuperPoint: Self-Supervised Interest Point Detection and Description
TL;DR: In this paper, a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision is presented, which operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass.
Proceedings ArticleDOI
Objects in Context
TL;DR: This work proposes to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model using a conditional random field (CRF) framework, which maximizes object label agreement according to contextual relevance.