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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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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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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.
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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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Fast Yet Effective Machine Unlearning.

Ayush K Tarun, Vikram S Chundawat, Murari Mandal

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    This summary is machine-generated.

    This study introduces a novel machine unlearning framework that efficiently removes specific data classes from trained models without retraining. The method achieves fast, scalable, and accurate unlearning for enhanced privacy in machine learning applications.

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    Area of Science:

    • Machine Learning
    • Data Privacy
    • Artificial Intelligence

    Background:

    • Machine learning model unlearning is crucial for data privacy and security.
    • Existing unlearning methods can be slow, resource-intensive, and difficult to scale.
    • There is a need for efficient and generalizable unlearning techniques.

    Purpose of the Study:

    • To develop a fast and scalable machine unlearning framework.
    • To enable unlearning of single or multiple data classes without access to the full training dataset.
    • To generalize the unlearning process to various deep network architectures.

    Main Methods:

    • A novel framework utilizing error-maximizing noise generation and impair-repair weight manipulation.
    • Learning an error-maximizing noise matrix for targeted class unlearning.
    • Employing impair (high learning rate with noise) and repair steps for controlled weight adjustment.

    Main Results:

    • Demonstrated excellent unlearning with minimal update steps.
    • Substantially retained overall model accuracy post-unlearning.
    • Achieved scalability for unlearning multiple classes, comparable to single-class unlearning.

    Conclusions:

    • The proposed method offers an efficient, scalable, and generalizable solution for machine unlearning.
    • It facilitates fast and easy implementation of unlearning in deep networks.
    • The approach enhances privacy and security in ML applications without compromising performance.