Search Results for author:
Found 7 papers, 5 papers with code
Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance
The discovered concepts and pixels from the pruned networks are inconsistent with the original network -- a possible reason for the drop in performance.
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
ML model design either starts with an interpretable model or a Blackbox and explains it post hoc.
Distilling BlackBox to Interpretable models for Efficient Transfer Learning
Building generalizable AI models is one of the primary challenges in the healthcare domain.
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions.
Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models
The FOLs from the finetuned-BB-derived MoIE verify the elimination of the shortcut.
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays
The critical component in our framework is an anatomy-guided attention module that aids the downstream observation network in focusing on the relevant anatomical regions generated by the anatomy network.
High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging
We use these features to develop and compare various classification models that can discriminate between scans showing dopaminergic deficit, as in PD, from scans without the deficit, as in healthy normal or SWEDD.
