Google llc (20240127058). TRAINING NEURAL NETWORKS USING PRIORITY QUEUES simplified abstract
Contents
- 1 TRAINING NEURAL NETWORKS USING PRIORITY QUEUES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING NEURAL NETWORKS USING PRIORITY QUEUES - 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
TRAINING NEURAL NETWORKS USING PRIORITY QUEUES
Organization Name
Inventor(s)
Mohammad Norouzi of Richmond Hill (CA)
Daniel Aaron Abolafia of Sunnyvale CA (US)
Quoc V. Le of Sunnyvale CA (US)
TRAINING NEURAL NETWORKS USING PRIORITY QUEUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127058 titled 'TRAINING NEURAL NETWORKS USING PRIORITY QUEUES
Simplified Explanation
The patent application describes methods, systems, and apparatus for training a neural network using a priority queue. The process involves maintaining a set of previously generated output sequences, selecting sequences from this set, determining scores for each selected sequence, updating controller parameters based on these scores, generating new output sequences using the neural network, obtaining rewards for the new sequences, identifying the k output sequences with the highest rewards, and modifying the maintained data accordingly.
- Maintaining a set of previously generated output sequences
- Selecting sequences from the set based on certain criteria
- Updating controller parameters based on the selected sequences
- Generating new output sequences using a neural network
- Obtaining rewards for the new sequences
- Identifying the top k output sequences with the highest rewards
- Modifying the maintained data based on the identified sequences
Potential Applications
This technology could be applied in various fields such as natural language processing, image recognition, and autonomous systems.
Problems Solved
This technology helps in improving the efficiency and performance of neural network training by prioritizing sequences with higher rewards.
Benefits
The use of a priority queue in training neural networks can lead to faster convergence, better results, and more efficient use of computational resources.
Potential Commercial Applications
One potential commercial application of this technology could be in developing advanced AI systems for industries such as healthcare, finance, and robotics.
Possible Prior Art
Prior art in the field of neural network training includes various techniques for optimizing training processes, such as reinforcement learning and evolutionary algorithms.
Unanswered Questions
How does this technology compare to existing methods for training neural networks?
This article does not provide a direct comparison with other training methods, so it is unclear how this approach differs in terms of performance, efficiency, and scalability.
What are the specific parameters and criteria used for selecting output sequences in the priority queue?
The article does not delve into the specific details of the selection process, leaving room for further exploration into the decision-making criteria used in this method.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using a priority queue. one of the methods includes maintaining data identifying a set of k output sequences that were previously generated; selecting at least one of the output sequences from the set of output sequences; for each selected output sequence, determining a respective score; determining, for each selected sequence, a respective first update to the current values of the controller parameters; generating a batch of new output sequences using the controller neural network; obtaining a respective reward for each of the new output sequences; determining, from the new output sequences and the output sequences in the maintained data, the k output sequences that have the highest rewards; and modifying the maintained data.
