Amazon technologies, inc. (20240193420). LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION simplified abstract
Contents
- 1 LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION
Organization Name
Inventor(s)
Arijit Biswas of Bangalore (IN)
Subhajit Sanyal of Bangalore (IN)
LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193420 titled 'LOW-DIMENSIONAL NEURAL-NETWORK-BASED ENTITY REPRESENTATION
Simplified Explanation
The patent application describes a system and method for training a multitask neural network to generate low-dimensional entity representations based on sequences of events associated with the entity.
Key Features and Innovation
- Combination of an encoder with multiple decoders to form a multitask neural network.
- Encoder generates low-dimensional entity representations from sequences of events.
- Decoders perform different machine learning tasks to predict various attributes of the entity.
- Trained encoder captures different attribute signals of the entities for semantically meaningful representations.
Potential Applications
The technology can be applied in various fields such as:
- Natural language processing
- Image recognition
- Recommendation systems
- Anomaly detection
Problems Solved
- Efficient generation of entity representations from sequences of events.
- Ability to perform multiple machine learning tasks on entities simultaneously.
- Capturing diverse attribute signals of entities for better representation.
Benefits
- Improved accuracy in predicting entity attributes.
- Enhanced efficiency in training neural networks.
- Semantically meaningful entity representations for downstream tasks.
Commercial Applications
- Semantic search engines
- Personalized recommendation systems
- Fraud detection algorithms
- Customer behavior analysis tools
Prior Art
Readers can explore prior research on multitask neural networks, entity representation learning, and sequence modeling in machine learning literature.
Frequently Updated Research
Stay updated on advancements in multitask learning, neural network architectures, and entity representation techniques for improved performance.
Questions about the Technology
What are the key advantages of using a multitask neural network for entity representation learning?
A multitask neural network can efficiently capture diverse attributes of entities and generate semantically meaningful representations, leading to improved performance in various machine learning tasks.
How does the encoder-decoder architecture contribute to the effectiveness of the multitask neural network?
The encoder-decoder architecture allows for the extraction of low-dimensional entity representations from sequences of events, enabling the network to perform multiple tasks efficiently.
Original Abstract Submitted
systems and methods are disclosed to implement a neural network training system to train a multitask neural network (mnn) to generate a low-dimensional entity representation based on a sequence of events associated with the entity. in embodiments, an encoder is combined with a group of decoders to form a mnn to perform different machine learning tasks on entities. during training, the encoder takes a sequence of events in and generates a low-dimensional representation of the entity. the decoders then take the representation and perform different tasks to predict various attributes of the entity. as the mnn is trained to perform the different tasks, the encoder is also trained to generate entity representations that capture different attribute signals of the entities. the trained encoder may then be used to generate semantically meaningful entity representations for use with other machine learning systems.
