J
Jonathon Shlens
Researcher at Google
Publications - 116
Citations - 88633
Jonathon Shlens is an academic researcher from Google. The author has contributed to research in topics: Object detection & Artificial neural network. The author has an hindex of 53, co-authored 116 publications receiving 63492 citations. Previous affiliations of Jonathon Shlens include Salk Institute for Biological Studies.
Papers
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Journal ArticleDOI
Correlated firing among major ganglion cell types in primate retina.
Martin Greschner,Jonathon Shlens,Constantina Bakolitsa,Greg D. Field,Jeffrey L. Gauthier,Lauren H. Jepson,Alexander Sher,Alan Litke,E. J. Chichilnisky +8 more
TL;DR: Systematic, distance‐dependent correlations between different ganglion cell types in the retina are revealed, consistent with a model in which noise in cone photoreceptors propagates through common retinal circuitry, creating correlations among ganglions cell signals.
Book ChapterDOI
Naive-Student: Leveraging semi-supervised learning in video sequences for urban scene segmentation
Liang-Chieh Chen,Raphael Gontijo Lopes,Bowen Cheng,Maxwell D. Collins,Ekin D. Cubuk,Barret Zoph,Hartwig Adam,Jonathon Shlens +7 more
TL;DR: In this article, the authors leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation.
Posted Content
Visual Wake Words Dataset
TL;DR: A new dataset, Visual Wake Words, is presented that represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models.
Proceedings ArticleDOI
A Learned Representation for Scalable Vector Graphics
TL;DR: This work attempts to model the drawing process of fonts by building sequential generative models of vector graphics, which has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation.
Posted Content
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Scott Ettinger,Shuyang Cheng,Benjamin Caine,Chenxi Liu,Hang Zhao,Sabeek Pradhan,Yuning Chai,Benjamin Sapp,Charles R. Qi,Yin Zhou,Zoey Yang,Aurelien Chouard,Pei Sun,Jiquan Ngiam,Vijay K. Vasudevan,Alexander McCauley,Jonathon Shlens,Dragomir Anguelov +17 more
TL;DR: In this article, the authors introduce a large-scale interactive motion dataset with over 100,000 scenes, each 20 seconds long at 10 Hz, collected by mining for interesting interactions between vehicles, pedestrians, and cyclists across six cities within the United States.