Adaptation of XAI to Auto-tuning for Numerical Libraries

• URL: http://arxiv.org/abs/2405.10973v1
• Date: Sun, 12 May 2024 09:00:56 GMT
• Title: Adaptation of XAI to Auto-tuning for Numerical Libraries
• Authors: Shota Aoki, Takahiro Katagiri, Satoshi Ohshima, Masatoshi Kawai, Toru Nagai, Tetsuya Hoshino,
• Abstract summary: Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concerns have arisen regarding the unregulated utilization of artificial intelligence (AI) outputs, potentially leading to various societal issues. While humans routinely validate information, manually inspecting the vast volumes of AI-generated results is impractical. Therefore, automation and visualization are imperative. In this context, Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. Simultaneously, software auto-tuning (AT) technology has emerged, aiming to reduce the man-hours required for performance tuning in numerical calculations. AT is a potent tool for cost reduction during parameter optimization and high-performance programming for numerical computing. The synergy between AT mechanisms and AI technology is noteworthy, with AI finding extensive applications in AT. However, applying AI to AT mechanisms introduces challenges in AI model explainability. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations: performance parameter tuning of accuracy-guaranteed numerical calculations and sparse iterative algorithm.

Related papers

• Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
  Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space. In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
  arXiv  Detail & Related papers  (2024-10-02T02:20:42Z)
• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review [0.0]
  We review the eXplainable AI (XAI) tools and techniques in this context. We focus on their role in making AI decision-making transparent, particularly in critical scenarios where humans are involved. We discuss current limitations and potential future research that aims to balance explainability with model performance.
  arXiv  Detail & Related papers  (2024-04-17T17:49:38Z)
• Towards a general framework for improving the performance of classifiers using XAI methods [0.0]
  This paper proposes a framework for automatically improving the performance of pre-trained Deep Learning (DL) classifiers using XAI methods. We will call auto-encoder-based and encoder-decoder-based, and discuss their key aspects.
  arXiv  Detail & Related papers  (2024-03-15T15:04:20Z)
• Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators [77.89070422157178]
  Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.
  arXiv  Detail & Related papers  (2024-01-30T19:27:16Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Towards Explainable Artificial Intelligence in Banking and Financial Services [0.0]
  We study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We develop a digital dashboard to facilitate interacting with the algorithm results.
  arXiv  Detail & Related papers  (2021-12-14T08:02:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.