Jyoti Narwariya

Title: Innovations of Jyoti Narwariya

Introduction

Jyoti Narwariya is a prominent inventor based in Noida, India. She has made significant contributions to the field of neural networks and time series data analysis. With a total of two patents to her name, her work focuses on enhancing the capabilities of neural networks in handling complex data scenarios.

Latest Patents

Jyoti's latest patents include innovative methods for managing variable-dimensional time series data. One of her patents addresses the challenges faced by existing neural network approaches that assume a fixed input dimension. Her work introduces a neural network architecture that allows for zero-shot transfer learning, enabling robust inference for multivariate time series with previously unseen combinations of sensors. This is achieved through conditioning layers that utilize a 'conditioning vector' to summarize learned sensor embedding vectors.

Another significant patent involves a method and system for training a neural network for time series data classification. This method is particularly useful in K-shot scenarios where training data is limited. By processing a diverse set of task-specific data, her system generates updated parameters to train the neural network effectively, ensuring it can solve target tasks from various domains with minimal training samples.

Career Highlights

Jyoti Narwariya is currently employed at Tata Consultancy Services Limited, where she continues to push the boundaries of technology and innovation. Her expertise in neural networks and time series data classification has positioned her as a valuable asset in her field.

Collaborations

Some of her notable coworkers include Pankaj Malhotra and Lovekesh Vig, who collaborate with her on various projects within the company.

Conclusion

Jyoti Narwariya's contributions to neural networks and time series data classification demonstrate her innovative spirit and commitment to advancing technology. Her patents reflect her ability to address complex challenges in data analysis, making her a noteworthy inventor in the field.