30 citations · 52 across the 11 of their papers we have counts for
12 papers
Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarks
Manya Singh, Arjun Pakrashi
Human-model alignment is critical for trustworthy AI-assisted decision-making systems. Yet, most work evaluates model predictions against single ground-truth labels, overlooking th…
Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency
Manya Singh, Mark T. Keane, Arjun Pakrashi
Machine learning models should be robust, in the sense of remaining predictively consistent under permissible variations. A model's predictions should ideally remain unchanged when…
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Eoin Delaney, Arjun Pakrashi, Derek Greene +1
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their p…
Random Walk-steered Majority Undersampling
Payel Sadhukhan, Arjun Pakrashi, Brian Mac Namee
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes.…
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit +1
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significa…
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
Mehran H. Z. Bazargani, Arjun Pakrashi, Brian Mac Namee
Anomaly detection is a challenging problem in machine learning, and is even more so when dealing with instances that are captured in low-level, raw data representations without a w…
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings
John Mitros, Arjun Pakrashi, Brian Mac Namee
Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified pre…
CascadeML: An Automatic Neural Network Architecture Evolution and Training Algorithm for Multi-label Classification
Arjun Pakrashi, Brian Mac Namee
Multi-label classification is an approach which allows a datapoint to be labelled with more than one class at the same time. A common but trivial approach is to train individual bi…
ML-KFHE: Multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
Arjun Pakrashi, Brian Mac Namee
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored…
A Kalman filtering induced heuristic optimization based partitional data clustering
Arjun Pakrashi, Bidyut B. Chaudhuri
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, vario…
MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network
Arjun Pakrashi, Elham Alghamdi, Brian Mac Namee +1
Meetup.com is a global online platform which facilitates the organisation of meetups in different parts of the world. A meetup group typically focuses on one specific topic of inte…
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
Arjun Pakrashi, Brian Mac Namee
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the…