Deepmind technologies limited (20240346285). FEEDFORWARD GENERATIVE NEURAL NETWORKS simplified abstract
Contents
FEEDFORWARD GENERATIVE NEURAL NETWORKS
Organization Name
Inventor(s)
Aaron Gerard Antonius Van Den Oord of London (GB)
FEEDFORWARD GENERATIVE NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346285 titled 'FEEDFORWARD GENERATIVE NEURAL NETWORKS
Simplified Explanation:
This patent application describes a feedforward generative neural network that can produce multiple output samples of a specific type in a single inference, potentially based on a contextual input. For example, it could generate a speech waveform that verbalizes a given text segment, taking into account linguistic features.
- The innovation involves a feedforward generative neural network that can generate multiple output samples of a specific type in a single inference.
- The generation process may be conditioned on a contextual input, such as linguistic features in the case of verbalizing text segments.
- The technology could be applied to various fields where generating multiple output samples efficiently is required.
Potential Applications:
- Speech synthesis
- Image generation
- Music composition
- Data augmentation in machine learning
Problems Solved:
- Efficient generation of multiple output samples in a single inference
- Contextual conditioning for more accurate and relevant output generation
Benefits:
- Time-saving in generating multiple output samples
- Improved accuracy and relevance of generated outputs
- Enhanced flexibility in adapting to different contexts
Commercial Applications:
The technology could be valuable in industries such as speech recognition, content generation, and data augmentation for machine learning models. It could streamline processes and improve the quality of generated content, leading to more efficient workflows and better results.
Prior Art:
Readers interested in exploring prior art related to this technology could start by researching advancements in feedforward generative neural networks, contextual conditioning in neural networks, and multi-output sample generation techniques.
Frequently Updated Research:
Researchers are continually exploring ways to enhance the efficiency and accuracy of generative neural networks, particularly in the context of multi-output sample generation and contextual conditioning. Stay updated on recent studies and developments in this field for the latest advancements.
Questions about Feedforward Generative Neural Networks:
1. How do feedforward generative neural networks differ from other types of generative models? 2. What are some potential challenges in implementing contextual conditioning in neural networks?
Original Abstract Submitted
a feedforward generative neural network that generates an output example that includes multiple output samples of a particular type in a single neural network inference. optionally, the generation may be conditioned on a context input. for example, the feedforward generative neural network may generate a speech waveform that is a verbalization of an input text segment conditioned on linguistic features of the text segment.
