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相关概念视频

Observational Learning01:12

Observational Learning

222
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...
222
Associative Learning01:27

Associative Learning

461
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...
461
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

132
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...
132
Introduction to Learning01:18

Introduction to Learning

478
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...
478
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

282
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
282
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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关于持续学习的顺序贝叶斯推理.

Samuel Kessler1, Adam Cobb2, Tim G J Rudner3

  • 1Department of Engineering Science, University of Oxford, Oxford OX2 6ED, UK.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

序列贝叶斯推理在贝叶斯神经网络中与灾难性遗忘作斗争. 一个新的原型贝叶斯持续学习基线显示了计算机视觉基准的竞争性表现.

关键词:
贝叶斯深度学习是贝叶斯的深度学习.贝叶斯神经网络是一个贝叶斯神经网络.持续的学习,持续的学习.终身学习是一项终身学习.序列贝叶斯推理 序列贝叶斯推理

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科学领域:

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

背景情况:

  • 持续学习的目的是培训模型进行连续的任务,而不会忘记过去的知识.
  • 序列贝叶斯推理提供了一个理论框架,通过使用先前的后者作为信息的前者来进行持续学习.
  • 贝叶斯神经网络因其概率性而被探索其在持续学习中的潜力.

研究的目的:

  • 评估序列贝叶斯推理在贝叶斯神经网络中防止灾难性遗忘的有效性.
  • 研究在神经网络中应用顺序贝叶斯推理与哈密尔顿蒙特卡洛的挑战.
  • 提出和评估一个新的贝叶斯持续学习方法.

主要方法:

  • 使用哈密尔顿式蒙特卡洛进行了序列贝叶斯推理.
  • 在哈密尔顿式蒙特卡洛样本上使用密度估计器近似计算后部分布.
  • 用分析示例来研究模型错误规范和数据不平衡的影响.
  • 提出并评估了一种新方法,即原型贝叶斯持续学习.

主要成果:

  • 直接应用顺序贝叶斯推理与哈密尔顿蒙特卡洛推理未能防止贝叶斯神经网络的灾难性遗忘.
  • 模型错误规范和数据不平衡被确定为持续学习中的重大挑战.
  • 拟议的原型贝叶斯持续学习基线在类增量计算机视觉基准上取得了竞争性结果.

结论:

  • 对神经网络权重的顺序贝叶斯推理对于强大的持续学习是不够的.
  • 需要持续学习生成过程的概率模型.
  • 原型贝叶斯持续学习为课堂增量持续学习提供了一个有希望和有效的方法.