International business machines corporation (20240119275). CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING simplified abstract
Contents
- 1 CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING
Organization Name
international business machines corporation
Inventor(s)
Yi Chen Zhong of Shanghai (CN)
Yuan Yuan Ding of Shanghai (CN)
CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119275 titled 'CONTRASTIVE LEARNING BY DYNAMICALLY SELECTING DROPOUT RATIOS AND LOCATIONS BASED ON REINFORCEMENT LEARNING
Simplified Explanation
The patent application describes a method for contrastive learning using reinforcement learning to select dropout ratios and locations in a neural network.
- The method involves receiving training data with positive and negative samples, producing a dropout policy based on the data, and encoding the data to form embeddings.
- The dropout policy identifies connections between neurons to dropout, creating positive and negative sample embeddings.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and recommendation systems.
Problems Solved
This method helps improve the performance of neural networks by enhancing the learning process through contrastive learning.
Benefits
The benefits of this technology include increased accuracy, better generalization, and improved feature representation in neural networks.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced AI systems for various industries such as healthcare, finance, and e-commerce.
Possible Prior Art
Prior art in the field of contrastive learning and dropout optimization techniques may exist, but specific examples are not provided in the patent application.
Unanswered Questions
How does this method compare to existing dropout optimization techniques in terms of performance and efficiency?
This article does not provide a direct comparison with existing dropout optimization techniques, leaving the question of performance and efficiency differences unanswered.
What computational resources are required to implement this method effectively in real-world applications?
The patent application does not detail the computational resources needed for implementing this method, leaving this question unanswered.
Original Abstract Submitted
a method for contrastive learning by selecting dropout ratios and locations based on reinforcement learning includes receiving training data having a positive sample corresponding to a target and negative samples not corresponding to the target. a dropout policy for a neural network is produced based on the training data, where the dropout policy identifies at least one connection between neurons in the neural network to dropout. the training data is encoded, based on the dropout policy, to form embeddings, where the embeddings include multiple positive sample embeddings corresponding to the positive sample and multiple negative sample embedding corresponding to the negative samples.
