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Aditya Krishna Menon
Researcher at Google
Publications - 120
Citations - 6712
Aditya Krishna Menon is an academic researcher from Google. The author has contributed to research in topics: Computer science & Supervised learning. The author has an hindex of 30, co-authored 109 publications receiving 4620 citations. Previous affiliations of Aditya Krishna Menon include NICTA & Commonwealth Scientific and Industrial Research Organisation.
Papers
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Proceedings ArticleDOI
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
Giorgio Patrini,Giorgio Patrini,Alessandro Rozza,Aditya Krishna Menon,Aditya Krishna Menon,Richard Nock,Richard Nock,Richard Nock,Lizhen Qu,Lizhen Qu +9 more
TL;DR: In this article, a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise is presented, and two procedures for loss correction that are agnostic to both application domain and network architecture are proposed.
Proceedings ArticleDOI
AutoRec: Autoencoders Meet Collaborative Filtering
TL;DR: Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.
Book ChapterDOI
Link prediction via matrix factorization
TL;DR: The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores, and may be combined with optional explicit features for nodes or edges, which yields better performance.
Posted Content
Anomaly Detection using One-Class Neural Networks.
TL;DR: A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods.
The cost of fairness in binary classification
TL;DR: This work relates two existing fairness measures to cost-sensitive risks, and shows that for such costsensitive fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function.