no code implementations • 1 Dec 2018 • Kumar Sricharan, Ashok Srivastava
There have been several pieces of work to address this issue, including a number of approaches for building Bayesian neural networks, as well as closely related work on detection of out of distribution samples.
no code implementations • 1 Dec 2018 • Kumar Sricharan, Kumar Kallurupalli, Ashok Srivastava
In this paper, we make the following observation: in practice, the out of distribution samples are contained in the traffic that hits a deployed classifier.
2 code implementations • 12 Sep 2017 • Hui Ding, Kumar Sricharan, Rama Chellappa
To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity.
no code implementations • 19 Aug 2017 • Kumar Sricharan, Raja Bala, Matthew Shreve, Hui Ding, Kumar Saketh, Jin Sun
We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework.
no code implementations • 14 Jul 2017 • Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan
The work presented here applies deep learning to the task of automated cardiac auscultation, i. e. recognizing abnormalities in heart sounds.
no code implementations • 16 Feb 2017 • Elizabeth Hou, Kumar Sricharan, Alfred O. Hero
Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not.
Unsupervised Anomaly Detection Vocal Bursts Intensity Prediction
no code implementations • NeurIPS 2012 • Kumar Sricharan, Alfred O. Hero
In this paper, it is shown that for sufficiently smooth densities, an ensemble of kernel plug-in estimators can be combined via a weighted convex combination, such that the resulting weighted estimator has a superior parametric MSE rate of convergence of order $O(T^{-1})$.
no code implementations • NeurIPS 2011 • Kumar Sricharan, Alfred O. Hero
In this paper, we propose a novel bipartite k-nearest neighbor graph (BP-kNNG) anomaly detection scheme for estimating minimum volume sets.