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相关概念视频

Purposive Learning01:22

Purposive Learning

411
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
411
Language Development01:22

Language Development

801
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
801
Language01:16

Language

855
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
855
Components of Language01:24

Components of Language

715
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
715
Language and Cognition01:27

Language and Cognition

681
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
681
Observational Learning01:12

Observational Learning

782
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
782

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相关实验视频

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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一个语言spikformer从BERT学习知识蒸的知识蒸.

Changze Lv1, Tianlong Li1, Weiming Qiao1

  • 1School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.

Neural networks : the official journal of the International Neural Network Society
|December 25, 2025
PubMed
概括

斯派克BERT是一种新的尖端神经网络 (SNN) 方法,在语言任务上实现了BERT级别的性能,并大大降低了能源消耗. 这种方法增强了自然语言处理的深度SNN.

关键词:
贝尔特 (BERT) 公司知识的蒸知识的蒸.语言理解 语言理解尖的神经网络的神经网络.

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科学领域:

  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.
  • 自然语言处理自然语言处理.

背景情况:

  • 尖端神经网络 (SNN) 提供节能的深度学习,但由于浅层架构,语言任务性能滞后.
  • 与BERT等变压器模型相比,语言任务的现有SNN表现出性能差距.

研究的目的:

  • 为了增强 Spikformer,一个尖端的变压器,用于高级语言任务处理.
  • 开发一个两阶段的知识蒸方法,用于训练深度SNNs.

主要方法:

  • 改进了 Spikformer 对语言任务的架构.
  • 两个阶段的知识蒸:BERT预培训和微调.
  • 使用大型未标记和特定任务的数据集进行SpikeBERT培训.

主要成果:

  • 在SNNs中,SpikeBERT实现了最先进的性能.
  • 与BERT关于英语和中文文本分类的可比结果.
  • 显示的能源消耗明显低于BERT.

结论:

  • 提出的方法有效地训练深度SNN进行语言任务.
  • 斯派克伯特为NLP提供了一个可行的,节能替代的变压器模型.
  • 这项研究弥合了SNN和语言处理中的变压器之间的性能差距.