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

Genetic Drift03:33

Genetic Drift

42.8K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
42.8K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

9.0K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
9.0K
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
Random Variables01:09

Random Variables

17.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.2K
Steps in the Modeling Process01:14

Steps in the Modeling Process

601
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
601
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

61.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
61.6K

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 collection of speech data for the assessment of cognition remotely: Balancing ethical and practical challenges.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

A large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland.

GigaScience·2026
Same author

Effects of Dietary <i>Salvia sclarea</i> L. Extract Supplementation on the Gut Microbiota, and Serum Metabolome in Lambs.

Microorganisms·2026
Same author

A conversational multi-agent AI system for automated plant phenotyping.

Nature communications·2026
Same author

Light intensity and cage position affect meat quality by regulating intestinal flora, inflammation and oxidation in broilers.

Frontiers in microbiology·2026
Same author

Antipsychotic-induced weight gain in psychosis: causal mediation analysis and feasibility study of causal actionable prediction model development using counterfactuals to target obesity.

The British journal of psychiatry : the journal of mental science·2026
Same journal

Cross-linguistic patterns of cognitive biases in large language models: a comparative study in English, Hebrew, and Russian.

Frontiers in artificial intelligence·2026
Same journal

From human-like AI to user adoption: the role of trust, attitude, and social influence in shaping behavioral intention.

Frontiers in artificial intelligence·2026
Same journal

Building large-scale English-Romanian literary translation resources with open models.

Frontiers in artificial intelligence·2026
Same journal

Editorial: GenAI in healthcare: technologies, applications and evaluation.

Frontiers in artificial intelligence·2026
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 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

994

生殖模型是否学习罕见的生殖因子?

Fasih Haider1, Edward Moroshko1, Yuyang Xue1

  • 1School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom.

Frontiers in artificial intelligence
|December 8, 2025
PubMed
概括
此摘要是机器生成的。

像扩散模型 (DMs),生成对抗网络 (GANs) 和变异自编码器 (VAEs) 这样的生成模型倾向于记住罕见的数据因素. 光谱解可以帮助减少人工智能模型中的这种记忆.

关键词:
扩散模型 (DMs) 是一种扩散模型.生成性的对抗性网络 (GANs)产生的因素是产生因素.潜在变量是隐藏的变量.罕见的生成因素是罕见的生成因素.罕见的生成因子 (RGFs)变化自动编码器 (VAE) 是一种变化自动编码器.

相关实验视频

Last Updated: Jan 9, 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

994

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度生成模型 深度生成模型

背景情况:

  • 生成模型是无监督学习和数据可变性的关键人工智能工具.
  • 扩散模型 (DM),生成对抗网络 (GAN) 和变异自编码器 (VAE) 在生成现实的数据方面表现出色.
  • 了解这些模型如何处理罕见的生成因子是提高其强度的关键.

研究的目的:

  • 研究DM,GAN和VAE对罕见生成因子的内化和复制.
  • 确定这些罕见因素的记忆的根本原因.
  • 评估缓解策略,以提高生成模型性能.

主要方法:

  • 对DM,GAN和VAE进行系统的实证研究.
  • 分析模型如何处理不常见的数据变化.
  • 缓解技术的实验评估,如光谱解.

主要成果:

  • 观察到DM,GAN和VAE有显著的倾向记住罕见的生成因子.
  • 该研究确定了导致这种记忆行为的特定原因.
  • 发现光谱分离在一定程度上减轻了记忆能力.

结论:

  • 生成型模型对罕见的数据因素表现出记忆偏差.
  • 解决这种偏见对于提高人工智能生成数据的可靠性至关重要.
  • 对诸如光谱解等技术的进一步研究是有必要的.