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相关概念视频

Orthogonal Trajectories01:26

Orthogonal Trajectories

71
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
71
Interference and Diffraction02:18

Interference and Diffraction

52.6K
Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
52.6K
RNA Interference01:23

RNA Interference

28.2K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
28.2K
Interference and Decay01:16

Interference and Decay

486
Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
486
Sound Waves: Interference00:53

Sound Waves: Interference

4.8K
Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
4.8K
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

7.1K
When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...
7.1K

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相关实验视频

Updated: Feb 12, 2026

Calibration Procedures for Orthogonal Superposition Rheology
08:43

Calibration Procedures for Orthogonal Superposition Rheology

Published on: November 18, 2020

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解开LoRA干扰:稳固模型合并的直角子空间

Haobo Zhang1, Jiayu Zhou1

  • 1University of Michigan, Ann Arbor, USA.

Proceedings of the conference. Association for Computational Linguistics. Meeting
|February 11, 2026
PubMed
概括
此摘要是机器生成的。

我们介绍了强大的模型合并 (OSRM) 的直角子空间,以有效地合并多个低级适应 (LoRA) 模型. OSRM 防止了任务干扰,提高了性能,并保持了可靠模型合并的准确性.

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Resonance Fluorescence of an InGaAs Quantum Dot in a Planar Cavity Using Orthogonal Excitation and Detection
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Orthogonal Protein Purification Facilitated by a Small Bispecific Affinity Tag
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Orthogonal Protein Purification Facilitated by a Small Bispecific Affinity Tag

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相关实验视频

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Resonance Fluorescence of an InGaAs Quantum Dot in a Planar Cavity Using Orthogonal Excitation and Detection
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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 针对特定任务微调大型语言模型 (LMs) 可以提高性能,但会产生高部署和存储成本.
  • 模型合并旨在将多个特定任务的模型结合到一个单一的多任务模型中,而无需重新培训.
  • 现有的合并技术与使用低级适应 (LoRA) 微调的模型进行斗争,往往导致性能降低.

研究的目的:

  • 在合并LoRA微调模型时解决性能退化问题.
  • 提出一种新的方法,通过考虑模型参数和数据分布之间的相互作用,使得LoRA模型的强大合并成为可能.
  • 从单个任务特定模型创建多任务模型的效率和有效性.

主要方法:

  • 强大的模型合并 (OSRM) 的拟议直角子空间,在微调之前限制LoRA子空间.
  • 确保特定任务的更新不会对其他任务产生负面影响.
  • 集成OSRM与现有的合并算法,以最大限度地减少任务之间的干扰.

主要成果:

  • 与现有方法相比,OSRM显著提高了合并模型的性能.
  • 拟议的方法在合并后成功地保持了单任务准确性.
  • 跨各种数据集和LM的实验证明了OSRM在合并超参数方面的有效性和稳定性.
  • 对于合并超参数,OSRM显示了更好的稳定性.

结论:

  • 数据和参数之间的相互作用对于有效的模型合并至关重要,特别是在LoRA.
  • OSRM提供了一个插件解决方案,用于合并LoRA微调模型,增强多任务学习能力.
  • 该方法提供了一种切实可行的方法,可以降低部署成本,同时在多个任务中保持高性能.