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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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提供对资源有限的人工智能系统的弹性模型和方法.

Viacheslav Moskalenko1, Vyacheslav Kharchenko2, Serhii Semenov3

  • 1Department of Computer Science, Sumy State University, 116, Kharkivska Str., 40007 Sumy, Ukraine.

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概括

本研究介绍了动态神经网络,以提高人工智能 (AI) 系统在资源有限的环境中的弹性. 这种新方法提高了人工智能对故障和攻击的稳定性,同时大大降低了计算成本.

关键词:
敌对的攻击是对抗性的攻击.可负担得起的弹性.漂流的概念漂流的概念漂流的概念动态的深度神经网络.错误注入的错误注入方式坚固性 坚固性 坚固性

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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:

  • 人工智能 (AI) 越来越多地用于资源有限,安全关键的嵌入式系统.
  • 提高人工智能的抗扰能力至关重要,特别是当计算资源有限时.
  • 动态神经网络提供了减少资源消耗的潜力,但它们的弹性尚未得到充分探索.

研究的目的:

  • 提出一种新的模型架构和培训方法,整合动态神经网络以提高弹性.
  • 评估拟议方法在提高AI系统对各种干扰的稳定性方面的有效性.
  • 与传统技术相比,评估新方法的资源效率.

主要方法:

  • 开发一个集成动态神经网络的模型架构,专注于弹性.
  • 实施了一种培训方法,旨在增强对故障注入和对抗性攻击的稳定性.
  • 利用超级培训来提高应对任务变化的弹性.

主要成果:

  • 卷积网络弹性增加了24%,视觉变压器弹性在故障注入下增加了19.7%.
  • 在敌对攻击下,ResNet-110的弹性增加了16.9%,DeiT-S的弹性增加了21.6%.
  • 通过超级培训,节省了超过30%的计算资源,并平均提高了22%的应对任务变化的弹性.

结论:

  • 动态神经网络的拟议整合显著提高了资源有限的系统中的AI弹性.
  • 该方法对故障注入,对抗性攻击和任务更改提供了实质性的改进.
  • 这种方法为在安全关键的嵌入式应用中部署强大的AI提供了具有成本效益的解决方案.