3 citations · 5 across the 5 of their papers we have counts for
6 papers
Noise resilience of deterministic analog combinatorial optimization solvers
Clemens Gneiting, Farad Khoyratee, Enrico Rinaldi +3
Several continuous dynamical systems have recently been proposed as special-purpose analog computers designed to solve combinatorial optimization problems such as -SAT or the Is…
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings
Utkarsh Sarawgi, Rishab Khincha, Wazeer Zulfikar +2
Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight th…
Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays
Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash +2
In this paper, our focus is on constructing models to assist a clinician in the diagnosis of COVID-19 patients in situations where it is easier and cheaper to obtain X-ray data tha…
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
Rishab Khincha, Utkarsh Sarawgi, Wazeer Zulfikar +1
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have tried us…
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia
Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha +1
Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidenc…
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha +1
Understanding and quantifying uncertainty in black box Neural Networks (NNs) is critical when deployed in real-world settings such as healthcare. Recent works using Bayesian and no…