FIELD NOTES TOPIC

metrics

5 notes, newest first.

  1. Defining Decentralization: An Ontological Perspective

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    This research paper addresses the lack of a universal definition for decentralization in computer science and its related fields like security, AI, cloud infrastructure, IoT, etc. The authors propose a graph-based…

  2. Defining Decentralization: An Ontological Perspective

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    The authors address the lack of a universally accepted definition of decentralization in computer science, particularly in emerging fields like decentralized AI and machine learning. They propose a graph-based ontology…

  3. Explaining Reinforcement Learning Agents via Inductive Logic Programming

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    This research aims to improve Reinforcement Learning (RL) policies by making them more transparent and interpretable. It proposes objective and planning-oriented metrics for policy explainability in RL settings using…

  4. Reliable Evaluation of MRI Motion Correction: Dataset and Insights

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    The paper discusses the difficulty in evaluating methods for correcting motion artifacts in medical imaging due to the lack of ground-truth data. To address this issue, they introduce PMoC3D, a dataset of real-world…

  5. Litmus: Zero-Label, Code-Driven Metric Specification for Evaluating AI Systems

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    The paper presents Litmus, a system that creates evaluation metrics for AI pipelines by understanding the purpose of the code and asking questions about it. It was tested on three real AI pipelines and outperformed…