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Related Concept Videos

Observational Learning01:12

Observational Learning

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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...
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Purposive Learning01:22

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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...
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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.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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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.
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Human-in-the-Loop Low-Shot Learning.

Sen Wan, Yimin Hou, Feng Bao

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    This study introduces a novel Human-In-the-Loop Low-shot (HILL) learning framework. It enhances low-shot learning by using uncertainty assessment and reinforcement learning to handle out-of-distribution samples effectively.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Low-shot learning (LSL) struggles with limited data, especially when novel categories have diverse samples.
    • Existing LSL methods are vulnerable to out-of-distribution (OOD) samples during testing.
    • Human-in-the-loop systems can improve model robustness but require efficient integration.

    Purpose of the Study:

    • To develop a robust human-in-the-loop framework for low-shot learning.
    • To address the challenge of out-of-distribution samples in novel categories.
    • To enable end-to-end training of human-in-the-loop low-shot learning systems.

    Main Methods:

    • Augmented the low-shot learning system with an uncertainty assessment module to detect OOD samples.
    • Integrated active labeling by humans for detected OOD samples.
    • Employed the REINFORCE algorithm from reinforcement learning to optimize model parameters via policy gradient, enabling end-to-end trainability.

    Main Results:

    • The proposed Human-In-the-Loop Low-shot (HILL) learning framework demonstrated noticeable improvements over existing low-shot learning approaches.
    • The uncertainty assessment module effectively identified and handled OOD samples.
    • Reinforcement learning enabled end-to-end optimization, overcoming limitations of discrete uncertainty assessment.

    Conclusions:

    • The HILL framework offers a significant advancement in low-shot learning by robustly handling OOD samples through human-in-the-loop active learning.
    • Integrating reinforcement learning provides an effective mechanism for end-to-end training of such hybrid systems.
    • This approach enhances the reliability and performance of models in scenarios with limited and potentially diverse data.