Company Filing History:
Years Active: 2018-2024
Title: The Innovations of Ryota Tomioka
Introduction
Ryota Tomioka is a prominent inventor based in Cambridge, GB. He has made significant contributions to the field of machine learning and neural networks, holding a total of six patents. His work focuses on enhancing the efficiency and effectiveness of neural network training processes.
Latest Patents
One of Ryota Tomioka's latest patents is titled "Asynchronous Neural Network Training." This invention describes a neural network training apparatus that features a network of worker nodes, each storing a subgraph of the neural network to be trained. The apparatus includes a control node connected to the worker nodes, which sends training data instances to trigger parallelized message passing operations. These operations implement a training algorithm that trains the neural network, with some message passing operations asynchronously updating parameters of individual subgraphs at the worker nodes. Another notable patent is "Partitioning for an Execution Pipeline." This invention involves accessing a computation graph of a machine learning model from memory and using a constraint solver to compute a partition of the graph into ordered stages of an execution pipeline. This approach balances the execution cost of the stages during inference or training of the machine learning model.
Career Highlights
Ryota Tomioka is currently employed at Microsoft Technology Licensing, LLC, where he continues to innovate in the field of machine learning. His work has garnered attention for its potential to improve the performance of neural networks in various applications.
Collaborations
Ryota has collaborated with notable colleagues such as Sebastian Nowozin and Diane Bouchacourt, contributing to advancements in their shared field of expertise.
Conclusion
Ryota Tomioka's contributions to neural network training and machine learning are significant, showcasing his innovative spirit and dedication to advancing technology. His patents reflect a deep understanding of complex computational processes and their practical applications.