Search Results for author:
Found 8 papers, 1 papers with code
Building robust classifiers through generation of confident out of distribution examples
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.
Improving robustness of classifiers by training against live traffic
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.
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity.
Semi-supervised Conditional GANs
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.
Recognizing Abnormal Heart Sounds Using Deep Learning
The work presented here applies deep learning to the task of automated cardiac auscultation, i. e. recognizing abnormalities in heart sounds.
Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies
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
Ensemble weighted kernel estimators for multivariate entropy estimation
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 $O(T^{-1})$.
Efficient anomaly detection using bipartite k-NN graphs
In this paper, we propose a novel bipartite k-nearest neighbor graph (BP-kNNG) anomaly detection scheme for estimating minimum volume sets.
