Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.7K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.7K
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
Cognitive Learning01:21

Cognitive Learning

960
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...
960
Retrieval01:12

Retrieval

384
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
384
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The value of social robots supporting informal care: a discrete choice experiment among informal caregivers.

The European journal of health economics : HEPAC : health economics in prevention and care·2026
Same author

Stochastic virtual population in type 1 diabetes.

PloS one·2026
Same author

Improving the value of population health data for health policy and decision-making using machine learning algorithms in EQ-5D-5L index estimation.

Scientific reports·2026
Same author

Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral-Entropy Features and Hybrid AI in Performance Sports.

Sensors (Basel, Switzerland)·2026
Same author

Decentralized Prescribed-Time Control of Robotic Arm-Finger Systems for Grasping and Moving Tasks.

IEEE transactions on cybernetics·2025
Same author

The Validation of the Parental Self-Efficacy Scale for Diabetes Management Among Parents of Children Wearing a Continuous Glucose Monitoring Sensor.

Biomedicines·2025
Same journal

The TaMYB55-TaSnRK1α1-TabZIP9 module confers heat stress tolerance in wheat.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Superstatistics approach to turbulent circulation fluctuations.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A molecular timescale for evolution of cobamide biosynthesis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Pierre Chambon, a pioneer of molecular biology and gene regulation in eukaryotes.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Granulosa cell glycogen fuels the avascular corpus luteum.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Synthetic essentiality of TRAIL/TNFSF10 in VHL-deficient renal cell carcinoma.

Proceedings of the National Academy of Sciences of the United States of America·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

983

SR-LLM:一个增量符号回归框架,由基于LLM的检索增强生成驱动.

Zelin Guo1, Siqi Wang1, Yonglin Tian2

  • 1Department of Automation, Tsinghua University, Beijing 100084, China.

Proceedings of the National Academy of Sciences of the United States of America
|December 22, 2025
PubMed
概括
此摘要是机器生成的。

使用大型语言模型 (LLM) 和检索增强生成的符号回归 (SR) 可实现增量学习. 这种SR-LLM框架有效地利用先前的知识,从数据中发现复杂的,可解释的分析模型.

关键词:
大型语言模型.提取-增强生成的回收.象征性回归是一种象征性回归.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

相关实验视频

Last Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

983
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.2K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 符号回归 (SR) 对于从数据中发现分析模型至关重要.
  • 现有的SR算法在巨大的搜索空间中扎,限制了复杂表达式的发现.
  • 深度学习的进步重新引起了对分析建模的SR的兴趣.

研究的目的:

  • 引入SR-LLM,一个新的SR框架,利用大语言模型 (LLM) 和提取增强生成进行增量学习.
  • 通过整合先前的知识来增强复杂,可解释的分析表达式的发现.
  • 将框架应用于具有挑战性的领域,如人类汽车追踪行为分析.

主要方法:

  • SR-LLM将提取增强生成与LLM集成,用于增量学习.
  • 该框架将先前的信息组成为符号组,使用LLMs.
  • 深度强化学习将这些组组合起来,形成复杂的分析表达式.

主要成果:

  • 在标准的SR基准指标上,SR-LLM表现出卓越的性能.
  • 该框架成功地从经验数据中重新发现已知的汽车跟踪模型.
  • 发现了人类汽车追踪行为的新分析模型,显示了有效性和可解释性.

结论:

  • SR-LLM有效地利用先前的知识和过去的探索进行符号回归.
  • 该框架有助于发现复杂的,人类可以理解的分析模型.
  • SR-LLM提供了一个强大的方法,用于科学发现在各种领域,包括行为分析.