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相关概念视频

Optimization Problems01:26

Optimization Problems

8
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Detection of Black Holes01:10

Detection of Black Holes

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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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过错误标记的训练实例使用黑子优化和量子化.

Makoto Otsuka1,2, Kento Kodama3, Keisuke Morita3,4

  • 1LiLz Inc., Okinawa, Japan. m.otsuka@lilz.jp.

Scientific reports
|October 30, 2025
PubMed
概括

这项研究引入了一种新的方法,通过使用黑子优化和量子回火来清除错误标记的实例来清除杂的训练数据. 这种方法通过提高数据集质量来增强机器学习模型的概括性.

关键词:
黑盒子优化优化 黑盒子优化数据清理数据清理量子化是一种量子化.

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科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 量子计算是一种量子计算.

背景情况:

  • 在训练数据集中错误标记的实例会降低模型的概括性.
  • 有效的消除噪音策略对于现实应用至关重要.
  • 现有的方法可能缺乏可扩展性或处理噪音数据的效率.

研究的目的:

  • 提出一种强大而有效的方法,从受污染的培训数据集中删除错误标记的实例.
  • 通过提高培训数据质量来提高机器学习模型的概括能力.
  • 为了利用量子化来有效地采样高质量的培训子集.

主要方法:

  • 结合了基于替代模型的黑子优化 (BBO) 和后期处理.
  • 使用量子回火来有效地采样各种训练子集,并具有较低的验证误差.
  • 评估基于验证损失的过训练子集,并代地改进损失估计.

主要成果:

  • 拟议的方法有效地优先删除高风险错误标记的实例.
  • 与D-Wave的物理量子化器集成,与模拟化相比,显示出更快的优化和更高质量的子集.
  • 该方法提供了一个可扩展的框架,用于提高监督学习中的数据集质量.

结论:

  • 开发的方法对于监督学习任务是有效的,提高了数据集质量和模型概括性.
  • 量子化,特别是在物理硬件上,在优化速度和子集质量方面提供了优势.
  • 未来的工作包括将该方法应用于无监督学习,现实世界数据集和大规模实施.