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Andrej Karpathy
Researcher at Stanford University
Publications - 21
Citations - 55755
Andrej Karpathy is an academic researcher from Stanford University. The author has contributed to research in topics: Recurrent neural network & Object detection. The author has an hindex of 20, co-authored 20 publications receiving 41085 citations. Previous affiliations of Andrej Karpathy include University of British Columbia.
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Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Journal Article
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Michael S. Bernstein,Li Fei-Fei,Alexander C. Berg,Aditya Khosla +11 more
Proceedings ArticleDOI
Large-Scale Video Classification with Convolutional Neural Networks
TL;DR: This work studies multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggests a multiresolution, foveated architecture as a promising way of speeding up the training.
Proceedings ArticleDOI
Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy,Li Fei-Fei +1 more
TL;DR: A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.
Journal ArticleDOI
Deep Visual-Semantic Alignments for Generating Image Descriptions
Andrej Karpathy,Li Fei-Fei +1 more
TL;DR: A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.