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相关概念视频

Encoding01:19

Encoding

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
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Neural Circuits01:25

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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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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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嵌入隐藏层学习用于神经网络压缩.

Jia Cheng Hu1, Roberto Cavicchioli2, Alessandro Capotondi1

  • 1University of Modena and Reggio Emilia, Department of Physical, Computer and Mathematical Sciences, via G.Campi 213/b, Modena, 41125, Italy.

Neural networks : the official journal of the International Neural Network Society
|July 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了ShareBERT,一种用于神经网络的新型参数共享方法. 在保持高精度的同时,ShareBERT显著减少了模型大小,使其能够在受限制的设备上高效地部署.

关键词:
贝尔特 (BERT) 公司压缩 压缩是一种压缩.嵌入式 嵌入式降低参数减小的方法参数共享 - 参数共享

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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:

  • 大型语言模型 (LLM) 在资源有限的设备上存在部署挑战,因为它们的参数数量庞大.
  • 有效的模型压缩技术对于在边缘计算和嵌入式系统中实现LLM至关重要.

研究的目的:

  • 引入一种用于神经网络压缩的新型参数共享方法.
  • 开发一系列新的高效神经网络架构 (ShareBERT).
  • 证明拟议方法在降低模型尺寸,同时保持性能方面的有效性.

主要方法:

  • 提出了一个新的参数共享技术,利用嵌入矩阵来学习隐藏的层.
  • 基于这种方法引入了一个新的架构家族,ShareBERT.
  • 在各种神经架构和任务中,对多个语言基准进行了评估.

主要成果:

  • 在只有500万个参数的情况下,ShareBERT可以达到95.5%的BERT准确度,减少了21.9倍.
  • 压缩方法增强,而不是阻碍,表示学习能力.
  • 该方法在各种神经网络类型,层和任务中是稳健和灵活的.

结论:

  • 拟议的参数共享方法可以实现显著的模型压缩,从而导致接近零的参数架构.
  • 在低功耗和嵌入式设备上,ShareBERT促进了先进序列模型的高效部署.
  • 这种方法与现有的压缩技术是直角的,提供了协同作用的潜力.