Methods for Explainable Artificial Intelligence

Gerald Friedland | 18-ERD-021

Executive Summary

We are developing simple yet powerful tools to inspect machine-learning models and evaluate their alignment with the user's understanding of the data. This project will fundamentally advance the field of artificial intelligence (on which national security missions increasingly rely) by introducing tools for accountability, explainability, and debugging.

Publications, Presentations, Etc.

Choi, J., et al. 2019. "From Intra-Modal to Inter-Modal Space: Multi-Task Learning of Shared Representations for Cross-Modal Retrieval." IEEE BigMM 2019: 5th IEEE International Conference on Multimedia Big Data, Singapore, September 2019. LLNL-CONF-788499.

Friedland, G., et al. 2019. "A Practical Approach to Sizing Neural Networks." Technical Report, LLNL-TR-758456.