10 citations · 22 across the 17 of their papers we have counts for
5 papers · 1 filter
HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms
Uddeshya Upadhyay, Sairam Bade, Arjun Puranik +8
The automated analysis of medical time series, such as the electrocardiogram (ECG), electroencephalogram (EEG), pulse oximetry, etc, has the potential to serve as a valuable tool f…
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt +2
Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and…
The Manifold Hypothesis for Gradient-Based Explanations
Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata +1
When do gradient-based explanation algorithms provide perceptually-aligned explanations? We propose a criterion: the feature attributions need to be aligned with the tangent space…
Transformer Based Reinforcement Learning For Games
Uddeshya Upadhyay, Nikunj Shah, Sucheta Ravikanti +1
Recent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic met…
Removal of Batch Effects using Generative Adversarial Networks
Uddeshya Upadhyay, Arjun Jain
Many biological data analysis processes like Cytometry or Next Generation Sequencing (NGS) produce massive amounts of data which needs to be processed in batches for down-stream an…