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Related Concept Videos

Associative Learning01:27

Associative Learning

743
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...
743
Cognitive Learning01:21

Cognitive Learning

742
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...
742
Purposive Learning01:22

Purposive Learning

253
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...
253
Introduction to Learning01:18

Introduction to Learning

636
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...
636
Observational Learning01:12

Observational Learning

452
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...
452
Learning Disabilities01:25

Learning Disabilities

331
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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What and Where: Learn to Plug Adapters via NAS for Multidomain Learning.

Hanbin Zhao, Hao Zeng, Xin Qin

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    Summary
    This summary is machine-generated.

    This study introduces a novel approach to multidomain learning (MDL) using neural architecture search (NAS) to automatically design adapter modules and their placement, improving learning flexibility and efficiency.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Science

    Background:

    • Multidomain learning (MDL) aims to create effective, lightweight domain-specific adapter modules for common networks.
    • Current methods use fixed, handcrafted adapter designs, limiting flexibility and increasing computational load.

    Purpose of the Study:

    • To develop a data-driven approach for determining adapter module placement using neural architecture search (NAS).
    • To automatically discover optimal adapter module structures for diverse domains via an NAS-adapter module.

    Main Methods:

    • Implemented a neural architecture search (NAS) strategy to learn optimal adapter plugging points.
    • Introduced an NAS-adapter module to automatically design adapter structures within an NAS-driven learning framework.

    Main Results:

    • The proposed MDL model demonstrates effectiveness compared to existing methods.
    • Achieved comparable performance with improved learning flexibility and reduced computational intensiveness.

    Conclusions:

    • The NAS-driven approach offers a more flexible and efficient solution for multidomain learning.
    • Automating adapter design and placement through NAS significantly enhances MDL model performance.