37 citations · 129 across the 31 of their papers we have counts for
4 papers · 1 filter
Improving model calibration with accuracy versus uncertainty optimization
Ranganath Krishnan, Omesh Tickoo
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be ac…
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features
Ibrahima Ndiour, Nilesh Ahuja, Omesh Tickoo
This paper presents a principled approach for detecting out-of-distribution (OOD) samples in deep neural networks (DNN). Modeling probability distributions on deep features has rec…
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang +12
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
Bayesian deep neural networks (DNNs) can provide a mathematically grounded framework to quantify uncertainty in predictions from image captioning models. We propose a Bayesian vari…