18437202. LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS simplified abstract (GOOGLE LLC)
LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS
Organization Name
Inventor(s)
David Wilson Romero Guzman of Amstelveen (NL)
LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18437202 titled 'LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS
Simplified Explanation
The patent application describes methods, systems, and apparatus for jointly learning the architecture of a neural network during its training using differentiable parametric masks.
Key Features and Innovation
- Learning the architecture of a neural network during training.
- Use of differentiable parametric masks.
- Computer programs encoded on computer storage media.
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and autonomous vehicles.
Problems Solved
- Enhances the efficiency of neural network training.
- Improves the performance of neural networks.
- Facilitates the development of more complex neural network architectures.
Benefits
- Faster and more accurate neural network training.
- Increased adaptability of neural networks.
- Enables the creation of more sophisticated neural network models.
Commercial Applications
The technology can be utilized in industries such as healthcare, finance, and e-commerce for tasks like medical image analysis, fraud detection, and personalized recommendations.
Prior Art
Researchers can explore existing literature on neural network architecture learning and parametric masks to understand the evolution of this technology.
Frequently Updated Research
Stay updated on advancements in neural network architecture learning and parametric mask techniques to leverage the latest innovations in the field.
Questions about Neural Network Architecture Learning
How does the use of differentiable parametric masks improve neural network training?
Differentiable parametric masks allow for the adaptive modification of the neural network architecture during training, leading to enhanced performance and efficiency.
What are the potential challenges in implementing this technology across different industries?
Implementing this technology in various industries may require domain-specific customization and integration, along with addressing potential compatibility issues with existing systems.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for jointly learning the architecture of a neural network during the training of the neural network. In particular, the architecture of the neural network is learned using differentiable parametric masks.
