Related Experiment Videos
On-line learning with minimal degradation in feedforward networks
1Joint Res. Centre, Comm. of the Eur. Communities, Ispra.
IEEE Transactions on Neural Networks
|January 1, 1995
Summary
This study introduces Learning with Minimal Degradation (LMD), a novel neural network technique for efficient adaptation in nonstationary environments. LMD enables quick learning while preventing catastrophic forgetting, outperforming traditional backpropagation methods.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Neural Networks
Background:
- Nonstationary processes necessitate rapid adaptation in neural networks.
- Catastrophic forgetting remains a significant challenge in continual learning.
- Distributed representations offer benefits but can be vulnerable to forgetting.
Purpose of the Study:
- To present a new neural learning technique that balances rapid adaptation with the prevention of catastrophic forgetting.
- To formalize the problem as a constrained optimization task.
- To introduce the Learning with Minimal Degradation (LMD) algorithm.
Main Methods:
- Formalizing the problem as minimizing error on past patterns under a perfect encoding constraint for new patterns.
- Transforming the constrained optimization into an unconstrained problem using hidden-unit activations.
- Developing the Learning with Minimal Degradation (LMD) algorithm based on this formulation.
Main Results:
- LMD demonstrates superior performance compared to backpropagation in experimental evaluations.
- The study reveals a direct dependence of forgetting on the learning rate in backpropagation.
- Overtraining is identified as a factor influencing both forgetting and fault tolerance.
Conclusions:
- Learning with Minimal Degradation (LMD) offers an effective solution for continual learning in nonstationary environments.
- LMD successfully addresses the trade-off between adaptation speed and memory retention.
- Understanding the relationship between overtraining, forgetting, and fault tolerance is crucial for robust neural network design.
Related Concept Videos
01:18Reducing Line Loss
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
01:12Observational Learning
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 because...
01:18Introduction 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...
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
01:27Associative 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...
Classical conditioning, also known...
01:21Cognitive 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...
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...
01:14Avoidance Learning and Learned Helplessness
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...
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...