Search Results for author:
Found 8 papers, 4 papers with code
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific inquiry.
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Through a large-scale human evaluation, we demonstrate that our technique discovers neuron groups that represent coherent, human-meaningful concepts.
A Comparative Analysis of Industry Human-AI Interaction Guidelines
We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis.
Human-Computer Interaction
Mapping Researchers with PeopleMap
Discovering research expertise at universities can be a difficult task.
PeopleMap: Visualization Tool for Mapping Out Researchers using Natural Language Processing
Discovering research expertise at institutions can be a difficult task.
Comparison of Syntactic and Semantic Representations of Programs in Neural Embeddings
Neural approaches to program synthesis and understanding have proliferated widely in the last few years; at the same time graph based neural networks have become a promising new tool.
RECAST: Interactive Auditing of Automatic Toxicity Detection Models
As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments.
