Google llc (20240211759). USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK simplified abstract

USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK

Organization Name

google llc

Inventor(s)

Gregory S. Corrado of San Francisco CA (US)

Kai Chen of San Bruno CA (US)

Jeffrey A. Dean of Palo Alto CA (US)

Gary R. Holt of Murrysville PA (US)

Julian P. Grady of Pittsburgh PA (US)

Sharat Chikkerur of Pittsburgh PA (US)

David W. Sculley, Ii of Pittsburgh PA (US)

USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240211759 titled 'USING EMBEDDING FUNCTIONS WITH A DEEP NETWORK

Simplified Explanation

The patent application describes methods, systems, and apparatus for using embedded functions with a deep network. This involves processing input features using embedding functions and a deep network to generate alternative representations and predict labels.

• Receiving input with different feature types
• Processing features with embedding functions independently
• Using a deep network for non-linear operations
• Predicting labels with logistic regression classifier

Key Features and Innovation

• Utilizes embedded functions with a deep network for processing input features
• Independent processing of features using embedding functions
• Deep network composed of multiple levels of non-linear operations
• Predicts labels for input using logistic regression classifier

Potential Applications

• Natural language processing
• Image recognition
• Sentiment analysis
• Recommendation systems
• Fraud detection

Problems Solved

• Efficient processing of input features with different types
• Improved accuracy in predicting labels for input data
• Enhanced performance in machine learning tasks

Benefits

• Enhanced accuracy in predicting labels
• Improved efficiency in processing input features
• Versatile application in various machine learning tasks

Commercial Applications

Artificial Intelligence in Predictive Analytics: Enhancing accuracy and efficiency in predictive analytics tasks using embedded functions and deep networks.

Prior Art

Readers can explore prior research on embedded functions, deep networks, and logistic regression classifiers in machine learning applications.

Frequently Updated Research

Stay updated on advancements in embedded functions, deep networks, and logistic regression classifiers in machine learning for improved predictive analytics.

Questions about Artificial Intelligence in Predictive Analytics

How does the use of embedded functions improve the processing of input features in machine learning tasks?

Embedded functions enhance the processing of input features by generating numeric values independently for each feature type, improving the efficiency and accuracy of the overall prediction process.

What are the advantages of using a deep network for non-linear operations in predicting labels for input data?

A deep network allows for complex patterns and relationships in the input data to be captured, leading to more accurate predictions and improved performance in machine learning tasks.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. one of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.