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相关概念视频

Observational Learning01:12

Observational Learning

281
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...
281
Reinforcement Schedules01:24

Reinforcement Schedules

231
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
231
Reinforcement01:23

Reinforcement

313
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
313
Associative Learning01:27

Associative Learning

525
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...
525
Machines: Problem Solving II01:30

Machines: Problem Solving II

355
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
355
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

730
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
730

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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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通过强化学习发现更快的矩阵乘法算法

Alhussein Fawzi1, Matej Balog2, Aja Huang2

  • 1DeepMind, London, UK. afawzi@deepmind.com.

Nature
|October 5, 2022
PubMed
概括
此摘要是机器生成的。

通过AlphaTensor,深度强化学习发现了新的矩阵乘法算法. 这种人工智能方法显著提高了计算效率,超过了关键矩阵大小的人为设计的方法.

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科学领域:

  • 计算机科学
  • 人工智能
  • 计算数学

背景情况:

  • 矩阵乘法是影响神经网络和科学计算等不同领域的基本计算.
  • 由于广的搜索空间,发现新的矩阵乘法算法具有挑战性.
  • 现有的算法虽然有效,但可能不是最佳解决方案.

研究的目的:

  • 开发一种人工智能驱动的方法来发现高效且可证明正确的矩阵乘法算法.
  • 探索深度强化学习在算法发现自动化方面的潜力.
  • 在矩阵复杂度方面取得突破,

主要方法:

  • 使用深度强化学习剂AlphaTensor,
  • 训练AlphaTensor玩一个专注于在有限的因子空间内找到张量分解的游戏.
  • 应用该代理来发现任意和结构化矩阵乘法算法.

主要成果:

  • AlphaTensor发现了超越各种矩阵维度的最先进复杂度的算法.
  • 对于有限场中的4x4矩阵的新算法改进了斯特拉森50年前的方法.
  • 对特定硬件运行时间和结构化矩阵乘法进行了优化.

结论:

  • 深度强化学习可以加速算法发现.
  • 这种方法为基本的计算任务提供了超越人类设计的算法的途径.
  • AlphaTensor提供了一个灵活的框架,可以根据不同的标准优化算法,包括计算复杂性和实际效率.