Google llc (20240265253). REGULARIZING MACHINE LEARNING MODELS simplified abstract
Contents
REGULARIZING MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Sergey Ioffe of Mountain View CA (US)
REGULARIZING MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265253 titled 'REGULARIZING MACHINE LEARNING MODELS
The patent application describes methods, systems, and apparatus for training a neural network to process input data items and generate scores for multiple labels.
- Obtaining a set of training data associated with labels from a predetermined set.
- Modifying the training data to create regularizing training data by changing labels for each training item.
- Training the neural network on the regularizing data.
- Key Features and Innovation:**
- Training a neural network to process input data items and generate scores for multiple labels.
- Modifying training data to create regularizing training data by changing labels for each training item.
- Potential Applications:**
This technology can be applied in various fields such as natural language processing, image recognition, and recommendation systems.
- Problems Solved:**
This technology addresses the need for efficient training of neural networks with multiple labels.
- Benefits:**
- Improved accuracy in processing input data items.
- Enhanced performance of neural networks in generating scores for multiple labels.
- Commercial Applications:**
Potential commercial uses include improving customer recommendation systems, enhancing image recognition software, and optimizing natural language processing algorithms for various industries.
- Prior Art:**
Researchers can explore prior art related to training neural networks with multiple labels and modifying training data for regularization techniques.
- Frequently Updated Research:**
Stay updated on the latest research in training neural networks with multiple labels and optimizing training data for improved performance.
- Questions about Neural Network Training with Multiple Labels:**
1. How does modifying training data to create regularizing training data impact the performance of the neural network? 2. What are the potential challenges in training a neural network with multiple labels and how can they be addressed?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage medium, for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels. the method includes actions of obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data, comprising: for each training item, determining whether to modify the label associated with the training item, and changing the label associated with the training item to a different label from the predetermined set of labels, and training the neural network on the regularizing data.
