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For the Inventor, By the Inventor

Lexington, MA, United States of America

Jean-Baptiste Tristan

USPTO Granted Patents = 10 

Average Co-Inventor Count = 2.7

ph-index = 2

Forward Citations = 14(Granted Patents)


Location History:

  • Cambridge, MA (US) (2019 - 2020)
  • Lexington, MA (US) (2017 - 2023)

Company Filing History:

goldMedal10 out of 11,294 
 
Oracle International Corporation
 patents

Years Active: 2017-2023

where 'Filed Patents' based on already Granted Patents

10 patents (USPTO):
1. 11636308 - Differentiable set to increase the memory capacity of recurrent neural net works (2023-04-25),
2. 11521069 - When output units must obey hard constraints (2022-12-06),
3. 11263541 - Ensembled decision systems using feature hashing models (2022-03-01),
4. 10860829 - Data-parallel parameter estimation of the Latent Dirichlet allocation model by greedy Gibbs sampling (2020-12-08),
5. 10496929 - Data-parallel probabilistic inference (2019-12-03),
6. 10394872 - Learning topics by simulation of a stochastic cellular automaton (2019-08-27),
7. 10157346 - Parallel Gibbs sampler using butterfly-patterned partial sums (2018-12-18),
8. 10147044 - Method and system for latent dirichlet allocation computation using approximate counters (2018-12-04),
9. 10140281 - Method and system for distributed latent dirichlet allocation computation using addition of approximate counters (2018-11-27),
10. 9767416 - Sparse and data-parallel inference method and system for the latent Dirichlet allocation model (2017-09-19),

Title: Innovations of Jean-Baptiste Tristan

Introduction

Jean-Baptiste Tristan is a notable inventor based in Lexington, MA (US). He has made significant contributions to the field of neural networks, holding a total of 10 patents. His work focuses on enhancing the capabilities of recurrent neural networks (RNNs) through innovative data structures and optimization techniques.

Latest Patents

One of his latest patents involves a differentiable set designed to increase the memory capacity of recurrent neural networks. This invention equips RNNs with a set data structure whose operations are differentiable, allowing the network to store information for extended periods. The differentiable set can remember events in sequential data that may influence later events, enabling the RNN to classify sequences based on long dependencies. Additionally, this RNN can be trained effectively using backpropagation and gradient descent optimizations. Another significant patent addresses the enforcement of deterministic constraints on outputs in neural networks. This method optimizes the continuous weights of the network to ensure that outputs conform to global constraints without the need for post-processing or complex searches.

Career Highlights

Jean-Baptiste Tristan is currently employed at Oracle International Corporation, where he continues to innovate in the field of artificial intelligence and machine learning. His work has been instrumental in advancing the capabilities of neural networks, making them more efficient and effective in processing complex data.

Collaborations

Throughout his career, Jean-Baptiste has collaborated with esteemed colleagues, including Guy Lewis Steele, Jr. and Michael Louis Wick. These collaborations have further enriched his research and development efforts in the field.

Conclusion

Jean-Baptiste Tristan's contributions to neural network technology through his patents and work at Oracle International Corporation highlight his role as a leading inventor in the field. His innovative approaches continue to shape the future of artificial intelligence.

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