5 papers
Semi-supervised Deep Transfer for Regression without Domain Alignment
Mainak Biswas, Ambedkar Dukkipati, Devarajan Sridharan
Deep learning models deployed in real-world applications (e.g., medicine) face challenges because source models do not generalize well to domain-shifted target data. Many successfu…
Rescuing referral failures during automated diagnosis of domain-shifted medical images
Anuj Srivastava, Karm Patel, Pradeep Shenoy +1
The success of deep learning models deployed in the real world depends critically on their ability to generalize well across diverse data domains. Here, we address a fundamental ch…
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
Barath Mohan Umapathi, Kushal Chauhan, Pradeep Shenoy +1
Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been lar…
Robust outlier detection by de-biasing VAE likelihoods
Kushal Chauhan, Barath Mohan U, Pradeep Shenoy +2
Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep ge…
Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems
Karan Aggarwal, Uday Bondhugula
Sparse matrix-vector multiplication (SpMV) operations are commonly used in various scientific applications. The performance of the SpMV operation often depends on exploiting regula…