16 citations · 22 across the 3 of their papers we have counts for
3 papers
Representation Based Complexity Measures for Predicting Generalization in Deep Learning
Parth Natekar, Manik Sharma
Deep Neural Networks can generalize despite being significantly overparametrized. Recent research has tried to examine this phenomenon from various view points and to provide bound…
Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
Avinash Kori, Parth Natekar, Ganapathy Krishnamurthi +1
The black-box nature of deep learning models prevents them from being completely trusted in domains like biomedicine. Most explainability techniques do not capture the concept-base…
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis
Parth Natekar, Avinash Kori, Ganapathy Krishnamurthi
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods bas…