Deepmind technologies limited (20240378439). AUTO-REGRESSIVE NEURAL NETWORK SYSTEMS WITH A SOFT ATTENTION MECHANISM USING SUPPORT DATA PATCHES simplified abstract
Contents
AUTO-REGRESSIVE NEURAL NETWORK SYSTEMS WITH A SOFT ATTENTION MECHANISM USING SUPPORT DATA PATCHES
Organization Name
Inventor(s)
Aaron Gerard Antonius Van Den Oord of London (GB)
Danilo Jimenez Rezende of London (GB)
Joao Ferdinando Gomes De Freitas of London (GB)
Scott Ellison Reed of Atlanta GA (US)
AUTO-REGRESSIVE NEURAL NETWORK SYSTEMS WITH A SOFT ATTENTION MECHANISM USING SUPPORT DATA PATCHES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378439 titled 'AUTO-REGRESSIVE NEURAL NETWORK SYSTEMS WITH A SOFT ATTENTION MECHANISM USING SUPPORT DATA PATCHES
Simplified Explanation:
The patent application describes a system that uses a causal convolutional neural network to generate a series of data item values based on previously generated values. The system includes support memory with encoded data patches and a soft attention mechanism to focus on specific patches during value generation.
- Key Features and Innovation:
* Utilizes a causal convolutional neural network for autoregressive data item value generation. * Incorporates support memory with encoded data patches for reference during value generation. * Employs a soft attention mechanism to prioritize specific patches based on previous values. * Conditions network layers on support data patches weighted by attention scores.
Potential Applications: The technology can be applied in various fields such as natural language processing, image recognition, and time series forecasting where sequential data generation is required.
Problems Solved: The system addresses the challenge of generating sequential data items based on previous values while maintaining context and relevance.
Benefits:
- Enables accurate and context-aware sequential data generation.
- Improves performance in tasks requiring autoregressive modeling.
- Enhances the efficiency of neural network-based systems.
Commercial Applications: Potential commercial applications include predictive analytics, speech recognition systems, and personalized recommendation engines in e-commerce.
Prior Art: Readers interested in prior art related to this technology can explore research papers on autoregressive modeling, attention mechanisms in neural networks, and memory-augmented neural networks.
Frequently Updated Research: Stay updated on the latest advancements in causal convolutional neural networks, attention mechanisms, and memory-augmented neural networks for sequential data generation.
Questions about the Technology: 1. How does the soft attention mechanism improve the generation of sequential data items? 2. What are the key advantages of using a causal convolutional neural network for autoregressive modeling?
Original Abstract Submitted
a system comprising a causal convolutional neural network to autoregressively generate a succession of values of a data item conditioned upon previously generated values of the data item. the system includes support memory for a set of support data patches each of which comprises an encoding of an example data item. a soft attention mechanism attends to one or more patches when generating the current item value. the soft attention mechanism determines a set of scores for the support data patches, for example in the form of a soft attention query vector dependent upon the previously generated values of the data item. the soft attention query vector is used to query the memory. when generating the value of the data item at a current iteration layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores.
