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相关概念视频

Associative Learning01:27

Associative Learning

444
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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Observational Learning01:12

Observational Learning

210
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...
210
Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
423
Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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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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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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Ki-Cook:通过知识注入的学习,聚合多式调理表现.

Revathy Venkataramanan1, Swati Padhee2, Saini Rohan Rao3

  • 1Department of Computer Science, Artificial Intelligence Research Institute, University of South Carolina, Columbia, SC, United States.

Frontiers in big data
|August 9, 2023
PubMed
概括

这项研究介绍了Ki-Cook,这是一个新的网络,用于用成分和标题等详细信息对食谱进行聚类,改善食物图像检索和理解食谱相似性,特别是对于罕见的成分.

关键词:
聚类集群是指聚类的聚类.过程建模过程建模跨模式的检索检索.成分预测 预测成分预测知识注入的学习是知识注入的学习.多模式学习是多模式学习.代表性学习学习学习

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相关实验视频

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科学领域:

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

背景情况:

  • 交叉模式的食谱检索可以实现图像到文本和文本到图像的搜索.
  • 目前的方法基于类名的集群配方,由于类内部的变化和类之间的相似性,这是不够的.
  • 详细的配方信息,包括标题和成分,为相似性确定提供了更强大的基础.

研究的目的:

  • 开发一种用于集群配方表示的新方法,以捕捉更深层次的配方相似性.
  • 提高对未知的食物图像检索相关信息的准确性.
  • 通过专注于食材,特别是罕见的食材,提高对配方关系的理解.

主要方法:

  • 提出了一个知识透的多式联网代理学习网络,Ki-Cook.
  • 将食谱标题,食材 (重点是稀有食材) 和动作纳入表现学习中.
  • 在网络中使用成分图像来学习多式调理表示.
  • 在过程的程序属性上构建了网络.

主要成果:

  • 与基线模型相比,Ki-Cook模型在成分检索任务中显示了基本事实覆盖率的12%的改善,以及与基线模型相比,十字路口对欧盟的10%的改善.
  • 学习的表征平均含有比基线模型多15.33%的稀有成分.
  • 在隐藏空间中对类似食谱进行集群方面取得了39%的改进,Fleiss kappa得分为0.35,用于注释者间的协议.

结论:

  • 利用包括稀有成分在内的全面的配方细节,显著提高了配方相似性聚类和检索.
  • Ki-Cook网络提供了一种更有效的方法来学习多式联调配方表示.
  • 这项工作代表了从视觉和文本数据中理解和检索食谱信息的重大进步.