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Improving Translational Accuracy02:07

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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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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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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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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.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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语义对齐的对抗性进化三角形用于高可转移性视觉语言攻击.

Xiaojun Jia, Sensen Gao, Qing Guo

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    此摘要是机器生成的。

    本研究引入了一种新的方法,为视觉语言预训练 (VLP) 模型创建更有效的对抗示例 (AE). 通过增加多样性和使用语义特征空间,该方法显著提高了AEs对未见模型的可转移性.

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    相关实验视频

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    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 自然语言处理自然语言处理.

    背景情况:

    • 视觉语言预训练 (VLP) 模型很强大,但易受多式联络对抗示例 (AE) 的影响.
    • 提高AE在不同模型中的可转移性对于开发强大的VLP系统至关重要.
    • 现有的AE生成方法在可转移性方面提供了有限的改进.

    研究的目的:

    • 开发一种新的方法,用于为VLP模型生成更多可转移的对抗实例 (AE).
    • 增强VLP模型对抗对方攻击的稳定性.
    • 为了解决当前AE发电技术的局限性.

    主要方法:

    • 建议对抗性进化三角形,从清洁,历史和当前的对抗性例子中抽取样本,以增加AE多样性.
    • 引入了一个语义对齐的子空间,以减少特征冗余并改善特征匹配.
    • 在语义图像-文本特征对比空间中生成的AEs.

    主要成果:

    • 提出的方法显著提高了对抗性示例的可转移性.
    • 对抗性进化三角形和语义对齐的子空间有效地增强了对抗性的多样性,并减少了特征冗余.
    • 实验结果表明,与最先进的对抗性攻击方法相比,性能优越.

    结论:

    • 新的AE生成策略有效地提高了VLP模型的对抗性可转移性.
    • 利用对抗性进化三角形和语义特征空间为强大的AI开发提供了一个有希望的方向.
    • 拟议的方法提供了一种实用方法,用于识别和减轻VLP模型中的漏洞.