6 citations · 18 across the 6 of their papers we have counts for
6 papers
Back to the Baseline: Examining Baseline Effects on Explainability Metrics
Agustin Martin Picard, Thibaut Boissin, Varshini Subhash +2
Attribution methods are among the most prevalent techniques in Explainable Artificial Intelligence (XAI) and are usually evaluated and compared using Fidelity metrics, with Inserti…
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Varshini Subhash, Anna Bialas, Weiwei Pan +1
Transformer based large language models with emergent capabilities are becoming increasingly ubiquitous in society. However, the task of understanding and interpreting their intern…
TACHYON: Efficient Shared Memory Parallel Computation of Extremum Graphs
Abhijath Ande, Varshini Subhash, Vijay Natarajan
The extremum graph is a succinct representation of the Morse decomposition of a scalar field. It has increasingly become a useful data structure that supports topological feature d…
Can Large Language Models Change User Preference Adversarially?
Varshini Subhash
Pretrained large language models (LLMs) are becoming increasingly powerful and ubiquitous in mainstream applications such as being a personal assistant, a dialogue model, etc. As t…
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations
Zixi Chen, Varshini Subhash, Marton Havasi +2
Interpretability provides a means for humans to verify aspects of machine learning (ML) models and empower human+ML teaming in situations where the task cannot be fully automated.…
A GPU Parallel Algorithm for Computing Morse-Smale Complexes
Varshini Subhash, Karran Pandey, Vijay Natarajan
The Morse-Smale complex is a well studied topological structure that represents the gradient flow behavior between critical points of a scalar function. It supports multi-scale top…