Company Filing History:
Years Active: 2020-2023
Title: Innovations of David Nunes Raposo
Introduction
David Nunes Raposo is an accomplished inventor based in London, GB. He has made significant contributions to the field of neural networks and artificial intelligence. With a total of three patents to his name, Raposo is recognized for his innovative approaches to memory-based neural networks and reinforcement learning.
Latest Patents
One of his latest patents is titled "Neural networks with relational memory." This invention describes a system that includes one or more computers and storage devices. The system is designed to implement a memory and memory-based neural network. The memory is configured to store respective memory vectors at various locations. The neural network processes inputs at multiple time steps, updating the memory and generating outputs based on the updated information.
Another notable patent is "Reinforcement learning using a relational network for generating data encoding relationships between entities in an environment." This invention proposes a neural network system that extracts entity data from state data. The relational network parses this data using attention blocks, which transform the data into modified entity data. The output network then selects actions based on the processed data.
Career Highlights
David Nunes Raposo is currently employed at DeepMind Technologies Limited, a leading company in artificial intelligence research. His work focuses on advancing the capabilities of neural networks and their applications in various environments.
Collaborations
Raposo has collaborated with notable colleagues, including Adam Anthony Santoro and Jack William Rae. Their combined expertise contributes to the innovative projects at DeepMind Technologies Limited.
Conclusion
David Nunes Raposo is a prominent inventor whose work in neural networks and artificial intelligence continues to push the boundaries of technology. His patents reflect a deep understanding of complex systems and their potential applications.
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