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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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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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Cognitive Learning01:21

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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...
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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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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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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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning.

Jun Luo1, Shandong Wu1,2,3,4

  • 1Intelligent Systems Program, University of Pittsburgh.

IJCAI : Proceedings of the Conference
|May 16, 2023
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Summary
This summary is machine-generated.

This study introduces APPLE, a personalized federated learning (FL) framework. APPLE enhances model performance on non-IID data by adaptively learning client collaboration, outperforming existing personalized FL methods.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Conventional federated learning (FL) faces challenges with non-IID datasets due to its one-model-fits-all approach.
  • Personalized FL aims to address data distribution shifts in decentralized learning environments.

Purpose of the Study:

  • To propose APPLE, a personalized cross-silo FL framework that adaptively determines client model benefit.
  • To introduce a flexible training control method for balancing global and local objectives in personalized FL.

Main Methods:

  • Developed APPLE, a personalized FL framework with adaptive client benefit learning.
  • Implemented a flexible training control mechanism for global and local objective balancing.
  • Conducted empirical evaluations on benchmark and medical imaging datasets under non-IID settings.

Main Results:

  • APPLE demonstrated state-of-the-art performance compared to existing personalized FL approaches.
  • Evaluated convergence and generalization behaviors of the proposed framework.
  • Validated effectiveness across diverse datasets and non-IID scenarios.

Conclusions:

  • APPLE effectively mitigates challenges posed by non-IID data in federated learning.
  • The proposed framework offers superior performance and flexibility in personalized FL.
  • The study provides a robust solution for personalized federated learning applications.