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Related Concept Videos

Associative Learning01:27

Associative Learning

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...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
Cognitive Learning01:21

Cognitive Learning

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...
Introduction to Learning01:18

Introduction to Learning

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...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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.
In the absence of...

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Related Experiment Video

Updated: Jul 7, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Learning in multilayered networks used as autoassociators.

M Bianchini1, P Frasconi, M Gori

  • 1Dipartimento de Sistemi e Inf., Firenze Univ.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
Summary

Gradient descent algorithms can get stuck in local minima. This study reveals a condition in multilayered autoassociators with geometric meaning in pattern space, improving learning.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Gradient descent algorithms are prone to local minima, leading to suboptimal learning in neural networks.
  • Multilayered networks are widely used as autoassociators for pattern recognition and data compression.

Purpose of the Study:

  • To investigate the learning process of multilayered autoassociators using gradient descent.
  • To establish relationships between multilayered autoassociators and classical linear autoassociators.
  • To derive and interpret a condition signifying the end of the learning process.

Main Methods:

  • Analysis of multilayered networks as autoassociators.
  • Comparison with classical linear autoassociators.
  • Application of a theoretical framework from previous research to derive a learning termination condition.

Related Experiment Videos

Last Updated: Jul 7, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Main Results:

  • Demonstrated relationships between multilayered and linear autoassociators.
  • Derived a specific condition met at the conclusion of the learning process.
  • Interpreted this condition as having significant geometrical meaning within the pattern space.

Conclusions:

  • The derived condition provides a deeper understanding of the convergence properties of gradient descent in autoassociators.
  • The geometric interpretation offers new insights into the optimization landscape of multilayered networks.
  • This research contributes to more effective training of autoassociative models.