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相关概念视频

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Fascia, a thin layer of fibrous connective tissue, is distributed throughout the body. It demarcates and forms a supportive covering over skeletal muscles, bones, blood vessels, and organs. There are three main types of facia— superficial fascia, deep fascia, and subserous fascia. These are all present at different depths in the body. Fascia reduces the friction and permits muscles, joints, and organs to easily slide against each other, facilitating movement of the body and preventing...
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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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相关实验视频

Updated: Jun 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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FLAT:在NLP中融合层次表示,以实现更有效的转移学习.

Wenqiang Zhu1, Yang Li1, Chunyan Liu1

  • 1School of Computer Science and Technology, Nanjing University of Aeronautic and Astronautics, Nanjing, 211106, China.

Neural networks : the official journal of the International Neural Network Society
|August 19, 2024
PubMed
概括
此摘要是机器生成的。

参数高效转移学习 (PETL) 方法得到了FLAT的改进,FLAT单独结合了所有层预训练语言模型 (PLM) 的知识. 这种方法减少了内存的使用量,并提高了性能,特别是在低资源的设置.

关键词:
计算效率 计算效率 计算效率数据效率数据的效率.自然语言处理自然语言处理.参数高效的学习转移学习.

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 参数高效转移学习 (PETL) 方法为预先训练的语言模型 (PLM) 提供了高效的微调.
  • 现有的PETL方法经常在所有PLM层中应用统一的结构,忽视层特定的知识,同时由于通过冷的PLM反向传播而产生高的计算和内存成本.

研究的目的:

  • 提出FLAT,一种新的PETL方法,旨在单独利用来自PLM所有层的知识.
  • 通过将反向传播路径与结的PLM脱来提高转移学习效率.

主要方法:

  • FLAT将骨干PLM视为特征提取器,将所有层的特征集成到一个单独的侧网中.
  • 这种架构避免了通过冷的PLM反向传播,大大降低了内存需求.

主要成果:

  • 与其他调技术相比,FLAT在GLUE基准标准上的低资源场景中表现优越.
  • 它在高资源场景中实现了可比性能,同时仅使用0.53%的可训练参数,并通过BERTbase将GPU内存使用量减少3.2×.
  • 废弃性研究证实了拟议的聚变层在将PLM知识整合到下游任务中的有效性.

结论:

  • 通过利用层层的知识多样性,FLAT为参数高效的转移学习提供了更有效和有效的方法.
  • 该方法在减少微调大型语言模型的计算和内存开销方面取得了重大进展.