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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Associative Learning01:27

Associative 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.
Classical conditioning, also known...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Improving Translational Accuracy02:07

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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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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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可证明的不受限制的对抗性训练,不妥协的概括性.

Lilin Zhang, Ning Yang, Yanchao Sun

    IEEE transactions on pattern analysis and machine intelligence
    |May 14, 2024
    PubMed
    概括

    可验证无限制对抗训练 (PUAT) 增强了对抗无限制对抗示例 (UAE) 和受限制对抗示例 (RAE) 的模型稳定性. 这种新的对抗训练方法提高了通用性,同时防御各种对抗攻击.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 对抗性训练 (AT) 对于防御对抗性攻击至关重要.
    • 现有的AT方法与不受限制的对抗实例 (UAEs) 斗争,并且经常为了稳定性而牺牲概括性.

    研究的目的:

    • 提出一种新的对抗训练方法,即可证明的不受限制的对抗训练 (PUAT).
    • 增强对UAE和受限对抗示例 (RAE) 的对抗强度.
    • 为了在对抗性强度的同时提高标准的通用性.

    主要方法:

    • PUAT将UAE视为乱的未观察到的例子,并解决了AT中的分配分离问题.
    • 使用部分标记数据和新增的三代对抗网络 (GAN) 进行有效的阿联生成和自然数据分布捕获.
    • 将目标分类器的监督损失集成到分配对齐的对抗性损失中.

    主要成果:

    • 普阿特为阿联和沙特阿拉伯提供了全面的对抗性强度.
    • 该方法同时提高了目标分类器的标准通用性.
    • 理论分析和实验证实了PUAT在基准指标上的优势.

    结论:

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    • PUAT为现有的对抗训练方法的局限性提供了一个有希望的解决方案.
    • 这种方法有效地平衡了对抗性稳定性和标准通用性.
    • PUAT在强大的机器学习和对抗防御领域取得了进展.