Google llc (20240185025). Flexible Parameter Sharing for Multi-Task Learning simplified abstract
Contents
- 1 Flexible Parameter Sharing for Multi-Task Learning
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Flexible Parameter Sharing for Multi-Task 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
Flexible Parameter Sharing for Multi-Task Learning
Organization Name
Inventor(s)
Effrosyni Kokiopoulou of Horgen (CH)
Krzysztof Stanislaw Maziarz of Cambridge (GB)
Andrea Gesmundo of Zürich (CH)
Luciano Sbaiz of Gattikon (CH)
[[:Category:Gábor Bart�k of Zürich (CH)|Gábor Bart�k of Zürich (CH)]][[Category:Gábor Bart�k of Zürich (CH)]]
Flexible Parameter Sharing for Multi-Task Learning - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185025 titled 'Flexible Parameter Sharing for Multi-Task Learning
Simplified Explanation
The present invention relates to systems and methods for flexible parameter sharing for multi-task learning.
- Training method involves obtaining a test input, selecting a particular task from one or more tasks, and training a multi-task machine-learned model for the particular task.
- Forward pass is performed using the test input and one or more connection probability matrices to generate a sample distribution of test outputs.
- Components of the machine-learned model are trained based on the sample distribution.
- Backwards pass is performed to train a connection probability matrix of the multi-task machine-learned model using a straight-through gumbel-softmax approximation.
Potential Applications
This technology can be applied in various fields such as natural language processing, computer vision, and speech recognition.
Problems Solved
This technology solves the problem of efficiently training multi-task machine learning models while sharing parameters among different tasks.
Benefits
The benefits of this technology include improved model performance, reduced training time, and enhanced parameter sharing capabilities.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced AI systems for industries such as healthcare, finance, and autonomous vehicles.
Possible Prior Art
Prior art may include research on multi-task learning, parameter sharing in machine learning models, and gumbel-softmax approximation techniques.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications may include the need for large amounts of training data, potential overfitting to specific tasks, and computational complexity.
How does this technology compare to existing methods for multi-task learning?
This technology offers a more flexible approach to parameter sharing and training multi-task machine learning models compared to traditional methods. It allows for efficient sharing of parameters among tasks while maintaining model performance.
Original Abstract Submitted
systems and methods for flexible parameter sharing for multi-task learning are provided. a training method can include obtaining a test input, selecting a particular task from one or more tasks, and training a multi-task machine-learned model for the particular task by performing a forward pass using the test input and one or more connection probability matrices to generate a sample distribution of test outputs, training the components of the machine-learned model based at least in part on the sample distribution, and performing a backwards pass to train a connection probability matrix of the multi-task machine-learned model using a straight-through gumbel-softmax approximation.
