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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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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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Associative Learning01:27

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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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Distribution Reliability and Automation01:25

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Multimachine Stability01:25

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Observational Learning01:12

Observational 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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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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针对安全机器学习模型的稳定性一致对抗训练 更新

Daniele Angioni, Luca Demetrio, Maura Pintor

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    模型更新可能会降低性能,这个问题被称为负翻转. 本研究引入了强度一致的对抗训练,以在机器学习模型更新期间保持安全性和准确性,防止性能回归.

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    科学领域:

    • 机器学习 机器学习
    • 计算机视觉 计算机视觉
    • 网络安全 网络安全

    背景情况:

    • 机器学习模型需要定期更新以提高使用新数据和架构的精度.
    • 模型更新可以引入"负翻转",新模型在以前正确的输入上表现更差,降低用户体验.
    • 这些负面翻转也会影响对抗性稳定性,破坏安全的模型更新实践.

    研究的目的:

    • 在模型更新期间调查负翻转对对抗性稳定性的影响.
    • 提出一种新的方法,强度一致的对抗训练,以减轻对抗强度中的性能回归.
    • 建立一个理论框架,用于培训使用非回归约束的一致估计者.

    主要方法:

    • 使用对抗训练微调机器学习模型.
    • 在更新之前对未受到对抗性攻击影响的样本实施限制,以保持高稳定性.
    • 开发一个理论上有基础的框架,用于学习的非回归约束.

    主要成果:

    • 负翻转会影响准确性和对抗性强度,即使更新后整体性能有所改善.
    • 强度一致的对抗训练有效地减轻了对抗强度的负面反转.
    • 拟议的方法在保持一致的性能和安全性方面优于现有的基线方法.

    结论:

    • 由于负翻转,模型更新带来了重大挑战,影响了一般准确性和对抗性安全性.
    • 稳定性一致的对抗训练提供了一个可行的解决方案,以防止模型更新期间的性能回归.
    • 具有非回归约束的学习框架为开发更可靠的机器学习模型提供了理论上合理的方法.