Danilo Jimenez Rezende

USPTO Granted Patents = 12 (Patent Applications = 4)

EPO Patents = 6 (Patent Applications = 9)

Average Co-Inventor Count = 4.2

ph-index = 3

Forward Citations = 24(Granted Patents)

Company Filing History:

10 out of 308

Deepmind Technologies Limited

Years Active: 2016-2025

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12 patents (USPTO):Sort by Forward Citations

Abstract keywords

1. 12481893 - Scene understanding and generation using neural networks (2025-11-25),

2. 12373695 - Auto-regressive neural network systems with a soft attention mechanism using support data patches (2025-07-29),

3. 11966839 - Auto-regressive neural network systems with a soft attention mechanism using support data patches (2024-04-23),

4. 11587344 - Scene understanding and generation using neural networks (2023-02-21),

5. 11430123 - Sampling latent variables to generate multiple segmentations of an image (2022-08-30),

6. 11328183 - Imagination-based agent neural networks (2022-05-10),

7. 11062229 - Training latent variable machine learning models using multi-sample objectives (2021-07-13),

8. 10860928 - Generating output data items using template data items (2020-12-08),

9. 10776670 - Imagination-based agent neural networks (2020-09-15),

10. 10657436 - Generative neural networks (2020-05-19),

11. 10176424 - Generative neural networks (2019-01-08),

12. 9342781 - Signal processing systems (2016-05-17),

Title: Danilo Jimenez Rezende: Innovator in Neural Network Systems

Introduction

Danilo Jimenez Rezende is a prominent inventor based in London, GB. He has made significant contributions to the field of artificial intelligence, particularly in the development of neural network systems. With a total of 11 patents to his name, Rezende's work is at the forefront of innovation in machine learning technologies.

Latest Patents

One of his latest patents is titled "Auto-regressive neural network systems with a soft attention mechanism using support data patches." This innovative system comprises a causal convolutional neural network designed to autoregressively generate a succession of values of a data item, conditioned upon previously generated values. The system includes support memory for a set of support data patches, each encoding an example data item. A soft attention mechanism is employed to attend to one or more patches when generating the current item value. This mechanism determines a set of scores for the support data patches, utilizing a soft attention query vector that depends on the previously generated values. The query vector is used to query the memory, ensuring that the layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores during the generation of the current item value.

Career Highlights

Danilo Jimenez Rezende has worked with leading companies in the tech industry, including DeepMind Technologies Limited and Google Inc. His experience in these organizations has allowed him to refine his skills and contribute to groundbreaking projects in artificial intelligence.

Collaborations

Throughout his career, Rezende has collaborated with notable professionals in the field, including Oriol Vinyals and Ivo Danihelka. These collaborations have further enhanced his work and expanded the impact of his innovations.

Conclusion

Danilo Jimenez Rezende is a key figure in the advancement of neural network systems, with a strong portfolio of patents and a history of collaboration with industry leaders. His contributions continue to shape the future of artificial intelligence and machine learning technologies.

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