18 citations · 72 across the 39 of their papers we have counts for
15 papers · 1 filter
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar +1
In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based cl…
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
A key factor in effective Self-Supervised learning (SSL) is preventing dimensional collapse, where higher-dimensional representation spaces () span a lower-dimensional subspace.…
Targeting Negative Flips in Active Learning using Validation Sets
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second wa…
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults
Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada +6
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common k…
VOICE: Variance of Induced Contrastive Explanations to quantify Uncertainty in Neural Network Interpretability
Mohit Prabhushankar, Ghassan AlRegib
In this paper, we visualize and quantify the predictive uncertainty of gradient-based post hoc visual explanations for neural networks. Predictive uncertainty refers to the variabi…
Transitional Uncertainty with Layered Intermediate Predictions
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
In this paper, we discuss feature engineering for single-pass uncertainty estimation. For accurate uncertainty estimates, neural networks must extract differences in the feature sp…