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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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...
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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.
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一步一步的分布对齐的风格快速调整为源代码免费的跨域短拍学习.

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    此摘要是机器生成的。

    本研究为没有源数据的大型模型引入了无源跨域短拍学习 (SF-CDFSL). 拟议的逐步分布调整式样式提示调整 (StepSPT) 方法隐式地减少了域差距,以提高少数拍摄的学习性能.

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    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 跨领域的少量学习 (CDFSL) 方法与大型预训练模型 (LMs) 斗争,原因是无法访问源数据和培训策略.
    • 对CDFSL进行微调LM的计算成本昂贵,限制了实际应用.

    研究的目的:

    • 调查无源CDFSL (SF-CDFSL) 问题,使目标领域的少数射击学习 (FSL) 只使用预训练模型和有限的目标样本.
    • 解决隐式缩小领域差距的挑战,而无需访问源数据.

    主要方法:

    • 提出逐步分布对齐的风格提示调 (StepSPT),这是SF-CDFSL的一种新方法.
    • 使用风格提示符来调整目标样本以达到预期的分布.
    • 采用双相优化流程:外部流程用于逐步调整样式提示符的分布,内部流程用于分类器更新.

    主要成果:

    • 在五个数据集上,StepSPT展示了对现有的提示调整方法和最先进的方法的优越性.
    • 废弃研究证实了拟议的StepSPT方法的有效性.
    • 绩效分析突出了分销优化战略的有效性.

    结论:

    • StepSPT为SF-CDFSL提供了实用和有效的解决方案,特别是对于大型预训练模型.
    • 该方法通过优化预测分布来隐性地减少域间隙,克服无源场景的局限性.
    • 步骤SPT通过使LMs能够有效地适应新领域,从而推进了少量学习领域.