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相关概念视频

Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Flashbulb Memory01:16

Flashbulb Memory

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A flashbulb memory is a highly vivid and detailed memory, often linked to events of significant emotional impact. These memories stand out in contrast to everyday memories due to their clarity and the precision with which they are recalled. The strong emotions associated with the event act as a catalyst, ensuring that specific details, such as one's location, actions, and even peripheral elements, are etched into memory with remarkable accuracy. For example, many people can vividly recall...
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Buffers: Buffer Capacity01:09

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Buffer capacity is the quantitative measure of a buffer to resist the change in pH. As shown in the following equation, the buffer capacity, denoted by 'beta', is expressed as the number of moles of acid or base needed to change the pH of a one-liter buffer solution by 1 unit. Here, Ca and Cb indicate the number of moles of acid and base, respectively. Note that dpH represents the change in pH.
In the graph, pH is plotted as a function of the number of moles of base (Cb) added to a weak...
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Sensory Memory01:14

Sensory Memory

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Sensory memory captures information from the environment in its original form for a very brief duration, just long enough to be exposed to visual, auditory, and other senses. This type of memory is detailed and rich but quickly lost unless certain strategies are employed to transfer it into short-term or long-term memory. Sensory information is continuously bombarding the human brain, yet only a small fraction is absorbed, as most of it does not significantly impact daily life. For instance,...
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Working Memory01:24

Working Memory

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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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相关实验视频

Updated: Mar 14, 2026

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
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A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

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警告:2 MB内存以下的注意事项

Riccardo Bravin1, Massimo Pavan1, Hazem Hesham Yousef Shalby1

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, Milano, 20133, Italy.

Neural networks : the official journal of the International Neural Network Society
|March 12, 2026
PubMed
概括
此摘要是机器生成的。

EmbBERT是一个微小的语言模型 (TLM),旨在在内存受限的设备上高效地部署. 这种紧型变压器模型只使用2 MB内存实现了最先进的精度,性能优于较大的模型.

关键词:
有效的深度学习.硬件加速器的硬件加速器语言模型 语言模型模型的压缩压缩.自然语言处理自然语言处理.微小的机器学习.

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相关实验视频

Last Updated: Mar 14, 2026

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 边缘计算 边缘计算

背景情况:

  • 变压器架构虽然对于自然语言处理 (NLP) 具有强大功能,但具有显著的内存和计算需求.
  • 在可穿戴设备和物联网设备等超限设备上部署先进的NLP模型是具有挑战性的,因为内存 (兆字节) 是有限的.

研究的目的:

  • 介绍EmbBERT,一个微小的语言模型 (TLM),在架构上优化,以在边缘设备上实现极端效率.
  • 为了证明简化的变压器架构可以在严格的内存约束下保持高性能.

主要方法:

  • 设计的EmbBERT具有紧的嵌入层,精简的前块和高效的注意力机制.
  • 在TinyNLP基准和GLUE套件上对EmbBERT进行了评估,将其性能与较大的最先进 (SotA) 模型和大小相似的BERT和MAMBA变体进行了比较.
  • 评估了该模型对8位量子化的弹性及其在不同内存范围 (从子兆字节到数十兆字节) 的可扩展性.

主要成果:

  • 埃姆伯特只需要2MB的内存,其精度与拥有10倍以上内存的SotA模型相提并论.
  • 在NLP任务上表现优于大小相似的缩小型BERT和MAMBA模型.
  • 证明了对8位量子化的弹性,将内存足迹减少到781kB.
  • 展示了 EmbBERT 架构在各种内存约束中的可扩展性.

结论:

  • 高度简化的变压器架构在资源有限的情况下对边缘NLP任务有效.
  • 恩伯特为在内存有限的边缘设备上部署先进的NLP功能提供了可行的解决方案.
  • 拟议的架构和预培训策略有助于高效,准确的边缘AI.