Qualcomm incorporated (20240095504). CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING simplified abstract
Contents
- 1 CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE 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
CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING
Organization Name
Inventor(s)
Debasmit Das of San Diego CA (US)
Jamie Menjay Lin of San Diego CA (US)
CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095504 titled 'CONSTRAINED MASKING FOR SPARSIFICATION IN MACHINE LEARNING
Simplified Explanation
The present disclosure provides techniques and apparatus for feature masking in a neural network. A feature tensor is accessed, and a feature mask is generated using a masking subnetwork trained based on polarization and activation constraints. A masked feature tensor is then generated, and an output inference is produced using the neural network.
- Explanation of the patent/innovation:
- Access feature tensor in neural network
- Generate feature mask using masking subnetwork
- Train subnetwork based on polarization and activation constraints
- Generate masked feature tensor
- Produce output inference using neural network
Potential Applications
The technology can be applied in image recognition, natural language processing, and other machine learning tasks where feature masking can enhance model performance.
Problems Solved
- Improves model interpretability by masking certain features - Helps prevent overfitting by focusing on relevant features - Enhances model generalization by reducing noise in the input data
Benefits
- Increased accuracy and efficiency in neural network predictions - Improved model robustness and reliability - Enhanced understanding of model decision-making processes
Potential Commercial Applications
Optimizing advertising targeting, improving medical diagnosis accuracy, enhancing fraud detection systems
Possible Prior Art
Prior art may include techniques for feature selection and dimensionality reduction in machine learning models, as well as methods for enhancing model interpretability through feature visualization.
Unanswered Questions
How does this technology compare to existing feature masking methods in neural networks?
This article does not provide a direct comparison with other feature masking techniques in neural networks. Further research or experimentation may be needed to evaluate the effectiveness and efficiency of this approach compared to existing methods.
What are the computational requirements for implementing this feature masking technique in real-world applications?
The article does not delve into the computational resources needed to implement this technology. Understanding the computational costs and scalability of this approach is crucial for practical deployment in various industries.
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatus for feature masking. a feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. a masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.
