Google llc (20240296331). LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS simplified abstract

LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS

Organization Name

google llc

Inventor(s)

David Wilson Romero Guzman of Amstelveen (NL)

Neil Zeghidour of Paris (FR)

LEARNING NEURAL NETWORK ARCHITECTURES BY BACKPROPAGATION USING DIFFERENTIABLE MASKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296331 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 for learning.
• Computer programs encoded on computer storage media for implementation.

Potential Applications

The technology can be applied in various fields such as:

• Artificial intelligence
• Machine learning
• Data analysis
• Pattern recognition

Problems Solved

The technology addresses the following issues:

• Improving the efficiency of neural network training.
• Enhancing the adaptability of neural network architectures.
• Streamlining the process of neural network development.

Benefits

The technology offers the following benefits:

• Faster and more accurate neural network training.
• Increased flexibility in designing neural network architectures.
• Improved performance of neural networks in various applications.

Commercial Applications

• The technology can be utilized in industries such as:
• Healthcare for medical image analysis.
• Finance for fraud detection.
• Marketing for customer behavior analysis.

Questions about Neural Network Architecture

How does the use of differentiable parametric masks improve the learning process of neural network architectures?

The use of differentiable parametric masks allows for the dynamic adjustment of network structures during training, leading to more efficient and effective learning.

What are the potential implications of jointly learning the architecture of a neural network during training in real-world applications?

Jointly learning the architecture of a neural network during training can lead to improved performance, faster deployment, and increased adaptability in real-world applications.

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.