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相关概念视频

Feedback control systems01:26

Feedback control systems

346
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
346
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

476
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
476
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.6K
2.6K
Effects of feedback01:24

Effects of feedback

603
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
603
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

787
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
787

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相关实验视频

Updated: Jul 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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关于将专家反纳入模型更新的前景.

Valerie Chen1, Umang Bhatt2,3, Hoda Heidari1

  • 1Carnegie Mellon University, Pittsburgh, PA, USA.

Patterns (New York, N.Y.)
|July 31, 2023
PubMed
概括

机器学习 (ML) 实践者需要更好的方法来使用专家反来开发模型. 本次审查提出了一种分类学,以系统地将领域专业知识转化为ML更新,改善人类-AI合作.

科学领域:

  • 人工智能的人工智能
  • 人与计算机的交互
  • 机器学习 机器学习

背景情况:

  • 机器学习 (ML) 模型越来越需要与非技术专家的价值观和目标保持一致.
  • 将领域专业知识转化为有效的ML模型更新仍然是从业人员面临的挑战.
  • 现有的方法缺乏系统的方法,用于将专家反纳入ML开发中.

研究的目的:

  • 系统地捕捉和分类ML从业者和领域专家之间的互动.
  • 开发一个分类系统,将专家反类型与特定的ML更新策略相匹配.
  • 强调需要改进方法,将非技术专家的见解融入ML.

主要方法:

  • 审查现有关于机器学习和人机交互的文献.
  • 提出一种新的分类学来分类专家反和相应的ML更新.
  • 分析观察或域级的反如何可以告知数据集,损失函数或参数空间调整.

主要成果:

  • 介绍了一个结构化的分类,对专家反 (观察与域级) 和从业人员更新 (数据集,损失函数,参数空间) 进行分类.
  • 该审查发现,目前的研究中存在一个关于系统地纳入非技术专家反的缺口.
  • 拟议的框架有助于更清楚地理解ML中的反更新循环.

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结论:

  • 一种系统的方法,像拟议的分类学,对于有效的人类-AI在ML的协作至关重要.
  • 需要进行进一步的研究,以解决在将非技术专家反纳入ML工作流程中所发现的差距.
  • 提出了开放式问题,以指导未来在这个跨学科领域的研究.