305 citations · 423 across the 6 of their papers we have counts for
6 papers
SANEval: Open-Vocabulary Compositional Benchmarks with Failure-mode Diagnosis
Rishav Pramanik, Ian E. Nielsen, Jeff Smith +3
The rapid progress of text-to-image (T2I) models has unlocked unprecedented creative potential, yet their ability to faithfully render complex prompts involving multiple objects, a…
Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts
Aakash Tripathi, Ian E. Nielsen, Muhammad Umer +2
Transcription Factor Binding Site (TFBS) prediction is crucial for understanding gene regulation and various biological processes. This study introduces a novel Mixture of Experts…
Targeted Background Removal Creates Interpretable Feature Visualizations
Ian E. Nielsen, Erik Grundeland, Joseph Snedeker +2
Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of…
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Ian E. Nielsen, Ravi P. Ramachandran, Nidhal Bouaynaya +2
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning mode…
Transformers in Time-series Analysis: A Tutorial
Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi +3
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects o…
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Ian E. Nielsen, Dimah Dera, Ghulam Rasool +2
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decis…