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Related Concept Videos

Associative Learning01:27

Associative 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.
Classical conditioning, also known...
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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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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Introduction to Learning01:18

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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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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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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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Related Experiment Videos

A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning.

Hongjun Wang, Guanbin Li, Xiaobai Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 19, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM) to generate diverse adversarial examples, enhancing deep learning model robustness. A new Contrastive Adversarial Training (CAT) method improves efficiency and accuracy in adversarial defense.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Deep convolutional neural networks (CNNs) excel in computer vision but are vulnerable to adversarial examples.
    • Existing methods generate limited adversarial examples, hindering a full understanding of their data manifold.
    • Adversarial examples pose security risks to real-world systems.

    Purpose of the Study:

    • To develop a method for generating a diverse sequence of adversarial examples.
    • To improve the efficiency and accuracy of adversarial training.
    • To enhance the robustness of deep learning models against adversarial attacks.

    Main Methods:

    • Proposed Hamiltonian Monte Carlo with Accumulated Momentum (HMCAM) for generating sequential adversarial examples.
    • Introduced adaptive trajectory length control to improve Hamiltonian Monte Carlo (HMC) efficiency.
    • Developed Contrastive Adversarial Training (CAT) by modifying Contrastive Divergence (CD) for faster convergence to adversarial example distributions.

    Main Results:

    • HMCAM successfully generated diverse sequences of adversarial examples.
    • The adaptive trajectory control enhanced HMC efficiency.
    • CAT achieved a favorable trade-off between computational efficiency and accuracy in adversarial training.
    • The proposed methods demonstrated superiority on natural image datasets and practical systems.

    Conclusions:

    • The developed HMCAM and CAT methods offer effective solutions for generating diverse adversarial examples and improving adversarial training.
    • These advancements contribute to more robust deep learning models and enhanced system security.
    • The research provides a more comprehensive exploration of the adversarial example solution space.