Year: 2024
Type: conference-paper
Abstract: The recent success of machine learning (ML) models has led to increasing demands for model explanations - why a result was given - along with model predictions. Tree-based ML models are considered more explainable than deep neural networks and higher performers in several domains. However, algorithms computing model explanations are irregular and scale poorly with model size. While many custom accelerators for training and inference have been proposed, little attention has been paid to accelerating model explanations. This lack of explanatory capability has limited the use of these models for real-time decision-making systems in critical fields such as healthcare, autonomous operation and cybersecurity.
Institution: Hewlett-Packard (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Army Research Office
Awards: G1432373144, G7452299184, G7515587205
OpenAlex ID: W4409282865