31 citations · 60 across the 6 of their papers we have counts for
7 papers
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification
Bindya Venkatesh, Jayaraman J. Thiagarajan
Deep predictive models rely on human supervision in the form of labeled training data. Obtaining large amounts of annotated training data can be expensive and time consuming, and t…
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh +4
Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surr…
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan +1
The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous…
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli +1
The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generaliza…
Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning
Bindya Venkatesh, Jayaraman J. Thiagarajan
The role of uncertainty quantification (UQ) in deep learning has become crucial with growing use of predictive models in high-risk applications. Though a large class of methods exi…
Learn-By-Calibrating: Using Calibration as a Training Objective
Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan
Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial…