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Found 4 papers, 1 papers with code
XAIport: A Service Framework for the Early Adoption of XAI in AI Model Development
In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations.
Characterization of Large Language Model Development in the Datacenter
Large Language Models (LLMs) have presented impressive performance across several transformative tasks.
Analyzing the Quality Attributes of AI Vision Models in Open Repositories Under Adversarial Attacks
In addition to evaluating efficiency in terms of balanced accuracy and computing costs, adversarial attacks are potential threats to the robustness and explainability of AI models.
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries
However, this task is very challenging due to the small sizes of surgical instrument tips, and significant variance of surgical scenes across different procedures.
