VARAD ANANT PIMPALKHUTE has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240160949
    Abstract: Technical limitation of conventional Gradient-Based Meta Learners is their inability to adapt to scenarios where input tasks are sampled from multiple distributions. Training multiple models, with one model per distribution adds to the training time owing to increased compute. A method and system for generating meta-subnets for efficient model generalization in a multi-distribution scenario using Binary Mask Perceptron (BMP) technique or a Multi-modal Meta Supermasks (MMSUP) technique is provided. The BMP utilizes an adaptor which determines a binary mask, thus training only those layers which are relevant for given input distribution, leading to improved training accuracy in a cross-domain scenario. The MMSUP, further determines relevant subnets for each input distribution, thus, generalizing well as compared to standard MAML. The BMP and MMSUP, beat Multi-MAML in terms of training time as they train a single model on multiple distributions as opposed to Multi-MAML which trains multiple models.
    Type: Application
    Filed: August 23, 2023
    Publication date: May 16, 2024
    Applicant: Tata Consultancy Services Limited
    Inventors: Shruti Kunal KUNDE, Rekha SINGHAL, Varad Anant PIMPALKHUTE
  • Publication number: 20230419180
    Abstract: Hardly any work in literature attempts employing Function-as-a-Service (FaaS) or serverless architecture to accelerate the training or re-training process of meta-learning architectures. Embodiments of the present disclosure provide a method and system for meta learning using distributed training on serverless architecture. The system, interchangeably referred to as MetaFaaS, is a meta-learning based scalable architecture using serverless distributed setup. Hierarchical nature of gradient based architectures is leveraged to facilitate distributed training on the serverless architecture. Further, a compute-efficient architecture, efficient Adaptive Learning of hyperparameters for Fast Adaptation (eALFA) for meta-learning is provided. The serverless architecture based training of models during meta learning enables unlimited scalability and reduction of training time by using optimal number of serverless instances.
    Type: Application
    Filed: April 3, 2023
    Publication date: December 28, 2023
    Applicant: Tata Consultancy Services Limited