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相关概念视频

Machines01:19

Machines

336
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
336
Machines: Problem Solving II01:30

Machines: Problem Solving II

370
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.
370
Introduction to Learning01:18

Introduction to Learning

532
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...
532
Machines: Problem Solving I01:22

Machines: Problem Solving I

408
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
408
Purposive Learning01:22

Purposive Learning

207
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
207
Cognitive Learning01:21

Cognitive Learning

521
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...
521

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简单的终身学习机器

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    此摘要是机器生成的。

    终身学习旨在提高过去和未来任务的绩效. 代表性保证有效地实现了前向和后向转移,而不会忘记,优于其他方法.

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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
    • 计算机科学 计算机科学

    背景情况:

    • 终身学习旨在提高模型在当前,过去和未来的任务上的表现.
    • 传统的转移学习和持续学习方法经常在学习新任务时,难以忘记过去的任务表现.
    • 目前的终身学习研究重点是防止先前任务的绩效下降,可能设定目标太低.

    研究的目的:

    • 调查一种简单的方法,代表性整合,以实现终身学习的前进和后退转移.
    • 证明终身学习的目标应该是提高未来和过去任务的绩效.
    • 评估在不同数据集中提出的方法的有效性,并将其与参考算法进行比较.

    主要方法:

    • 该研究提出并评估了一种代表性整合方法,用于终身学习.
    • 该方法在各种模拟和基准数据集上进行了测试,包括表格,视觉 (CIFAR-100,5-数据集,Split Mini-Imagenet,Food1k,CORe50) 和语音 (口语数字) 数据.
    • 此外,还评估了该方法在计算预算限制方面的灵活性.

    主要成果:

    • 代表组合证明了显著的前向转移 (改善未来的任务性能) 和后向转移 (改善过去的任务性能).
    • 拟议的方法超过了各种参考算法,这些算法往往无法实现向前或向后转移,或者两者兼而有之.
    • 这种方法在广泛的数据模式和基准场景中被证明是有效的.

    结论:

    • 代表组合是一种简单而有力的技术,可以实现强大的终身学习.
    • 结果表明,终身学习系统应该积极利用过去的数据,以提高以前和未来任务的绩效.
    • 提出的方法为持续学习挑战提供了灵活有效的解决方案,可适应不同的计算资源.