V
Vinod Nair
Researcher at Google
Publications - 31
Citations - 16160
Vinod Nair is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Generative model. The author has an hindex of 17, co-authored 30 publications receiving 13717 citations. Previous affiliations of Vinod Nair include Malaviya National Institute of Technology, Jaipur & Yahoo!.
Papers
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Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
Proceedings Article
3D Object Recognition with Deep Belief Nets
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: A new type of top-level model for Deep Belief Nets is introduced, a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients that substantially outperforms shallow models such as SVMs.
Proceedings ArticleDOI
An unsupervised, online learning framework for moving object detection
Vinod Nair,James J. Clark +1 more
TL;DR: This work presents a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video with an online learner based on the Winnow algorithm.
Proceedings ArticleDOI
Learning hierarchical similarity metrics
TL;DR: A novel framework to learn similarity metrics using the class taxonomy is proposed and it is shown that a nearest neighbor classifier using the learned metrics gets improved performance over the best discriminative methods.
Proceedings ArticleDOI
A joint learning framework for attribute models and object descriptions
TL;DR: By incorporating class information into the attribute classifier learning, this work gets an attribute-level representation that generalizes well to both unseen examples of known classes and unseen classes.