6 citations · 6 across the 6 of their papers we have counts for
6 papers
TopoAlign: Topology-Aware Visual Representation Alignment
Xinyuan Yan, Rita Sevastjanova, Mennatallah El-Assady +1
Neural networks encode inputs as high-dimensional vectors, known as representations, that capture how models process data by encoding task-relevant structure and semantics. Represe…
Teaching People LLM's Errors and Getting it Right
Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan +3
People use large language models (LLMs) when they should not. This is partly because they see LLMs compose poems and answer intricate questions, so they understandably, but incorre…
Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agents
Xinyuan Yan, Rita Sevastjanova, Sinie van der Ben +2
Large language models (LLMs) produce high-dimensional embeddings that capture rich semantic and syntactic relationships between words, sentences, and concepts. Investigating the to…
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono +6
Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of…
EulerMerge: Simplifying Euler Diagrams Through Set Merges
Xinyuan Yan, Peter Rodgers, Peter Rottmann +3
Euler diagrams are an intuitive and popular method to visualize set-based data. In a Euler diagram, each set is represented as a closed curve, and set intersections are shown by cu…
Flexible and Probabilistic Topology Tracking with Partial Optimal Transport
Mingzhe Li, Xinyuan Yan, Lin Yan +2
In this paper, we present a flexible and probabilistic framework for tracking topological features in time-varying scalar fields using merge trees and partial optimal transport. Me…