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相关概念视频

Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
The Ideal Transformer01:26

The Ideal Transformer

1.3K
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
1.3K
Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

493
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
493

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相关实验视频

测试时间培训可以证明改进了变压器作为背景学习者.

Halil Alperen Gozeten1, M Emrullah Ildiz1, Xuechen Zhang1

  • 1University of Michigan, Ann Arbor.

Proceedings of machine learning research
|December 1, 2025
PubMed
概括

测试时间训练 (TTT) 通过更新权重来使模型适应特定数据. 这项研究揭开了TTT在上下文学习中的神秘性,表明它减少了对表格分类的样本需求.

科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 测试时间训练 (TTT) 方法将模型适应特定的测试实例,在语言和推理任务中表现出成功.
  • 语境学习 (ICL) 依赖于模型适应提示中的演示.
  • 分布转移对模型概括提出了挑战.

研究的目的:

  • 消除基于梯度的TTT在上下文学习中的成功.
  • 从理论上来说,在线性变压器中用单个梯度步骤来描述TTT.
  • 实证地评估TTT对TabPFN等表式基础模型的好处.

主要方法:

  • 在线变压器中开发了基于梯度的TTT理论框架.
  • 分析了预训练与目标任务对齐对TTT有效性的影响.
  • 量化了TTT提供的样本复杂性减少.
  • 用TabPFN模型对表格分类进行实证研究的TTT.

主要成果:

  • 该理论描述了TTT在减轻分布转移中的作用.
  • TTT显著减少了在上下文学习所需的样本大小.
  • 经验结果表明,TTT减少了对表格分类的样本需求的3-5倍.

相关实验视频

  • 在可忽略不计的培训成本下,实现了实质性的推断效率.
  • 结论:

    • TTT是一种强大的技术,可以使模型适应特定的测试实例,特别是在ICL设置中.
    • TTT有效地减轻了分布转移,并减少了样本的复杂性.
    • 在使用基础模型的表格分类任务中,TTT提供了显著的效率提升.