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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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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Introduction to Learning01:18

Introduction to Learning

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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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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Clustered Federated Learning in Heterogeneous Environment.

Yihan Yan, Xiaojun Tong, Shen Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 20, 2023
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    Summary
    This summary is machine-generated.

    Federated learning (FL) faces challenges with diverse data and systems. This study introduces an iterative clustered FL (ICFL) framework that adaptively discovers optimal clusters, improving model performance and convergence.

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    Area of Science:

    • Machine Learning
    • Distributed Systems
    • Data Science

    Background:

    • Federated learning (FL) enables collaborative model training without data sharing, preserving privacy.
    • High systems and statistical heterogeneity in FL clients cause model divergence and nonconvergence.
    • Clustered FL addresses statistical heterogeneity by forming groups of clients, but struggles with adaptive cluster number selection.

    Purpose of the Study:

    • To propose an iterative clustered FL (ICFL) framework for adaptive cluster discovery.
    • To address the limitations of existing clustered FL methods in environments with high systems' heterogeneity.
    • To improve the performance and convergence of FL models under diverse client conditions.

    Main Methods:

    • Developed an iterative clustered FL (ICFL) framework with dynamic cluster structure discovery.
    • Implemented incremental clustering and clustering methods compatible with ICFL, focusing on average intra-cluster connectivity.
    • Utilized mathematical analysis to guide the clustering methods within the ICFL framework.

    Main Results:

    • ICFL framework successfully discovers clustering structures dynamically.
    • Experimental results demonstrate ICFL's superior performance compared to baseline clustered FL methods.
    • The proposed methods are validated across datasets with high systems and statistical heterogeneity, and for convex/nonconvex objectives.

    Conclusions:

    • ICFL effectively handles systems and statistical heterogeneity in federated learning.
    • The adaptive clustering approach in ICFL leads to improved model performance and convergence.
    • This framework offers a robust solution for optimizing federated learning in complex, real-world scenarios.