5 citations · 16 across the 8 of their papers we have counts for
5 papers · 1 filter
Pretrained Encoders are All You Need
Mina Khan, P Srivatsa, Advait Rane +4
Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-…
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…
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…