Pedro Esperanca

Title: Pedro Esperanca and Deep Convolutional Neural Network Normalization

Introduction

Pedro Esperanca is an inventor based in Cambridge, Great Britain. His work includes a patent concerning normalization in deep convolutional neural networks, a technique aimed at improving the training and performance of machine-learning systems.

Latest Patents

Esperanca is associated with 1 patent: “Normalization in Deep Convolutional Neural Networks.” The invention describes a machine-learning device with two neural network layers and a normalization layer positioned between them. During training, the normalization layer receives outputs for multiple samples in a batch, groups data values according to indices across two dimensions, and produces normalized outputs for the next layer.

The patent application presents this approach as a way to train deep convolutional neural networks that perform well across different batch sizes and generalize to multiple vision tasks.

Career Highlights

Esperanca is associated with Huawei Technologies Co., Ltd. His patented work focuses on neural network training and normalization, addressing performance and stability across varying training conditions.

Collaborations

The provided information names Fabio Maria Carlucci and Jiacheng Sun as Esperanca’s coworkers. Their specific roles in the patented invention are not stated.

Conclusion

Pedro Esperanca’s patent describes a normalization method designed to support stable, effective training of deep convolutional neural networks across different batch sizes and vision tasks.