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相关概念视频

Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Neural Circuits01:25

Neural Circuits

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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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TinyNS:平台意识的神经象征自动微型机器学习

Swapnil Sayan Saha1, Sandeep Singh Sandha2, Mohit Aggarwal3

  • 1University of California - Los Angeles, Los Angeles, CA, USA.

ACM transactions on embedded computing systems : TECS
|June 27, 2024
PubMed
概括
此摘要是机器生成的。

TinyNS是一个新的框架,用于在资源有限的设备上创建可解释的AI系统. 它优化了符号推理和机器学习模型的边缘应用程序,优于传统方法.

关键词:
在AutoML中使用AutoML.贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语在TinyML中使用TinyML.神经架构搜索神经架构搜索这是神经符号神经符号.这是一个平台意识的平台.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 嵌入式系统 嵌入式系统

背景情况:

  • 在边缘设备上部署具有符号推理的可解释AI是由于资源限制而具有挑战性的.
  • 现有的方法在严格的硬件限制下努力平衡象征性完整性和机器学习性能.

研究的目的:

  • 介绍TinyNS,一个平台意识的神经符号架构搜索框架.
  • 为边缘AI应用程序实现符号和神经运算符的联合优化.
  • 促进在微控制器上部署的神经符号模型的创建.

主要方法:

  • 开发了TinyNS,用于用于神经符号模型的自动微控制器代码生成的框架.
  • 使用无梯度,黑盒贝叶斯优化器,在复杂空间中进行高效的搜索.
  • 集成的硬件意识优化,以确保现实世界的部署性.

主要成果:

  • 在几个案例研究中,TinyNS成功地部署了微控制器级神经符号模型.
  • 该框架自动生成五种类型的神经符号模型的代码.
  • 优化的神经符号模型与纯神经或符号方法相比,表现出更高的性能.

结论:

  • TinyNS有效地结合了象征性推理和机器学习,用于边缘AI.
  • 该框架保证在真实硬件上执行,克服部署挑战.
  • TinyNS代表了开发强大的和可解释的AI在资源有限的环境中的重大进步.