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Data-efficient Deep Learning Algorithms for Computer Vision Applications
The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ...
Data Driven Stabilization Schemes for Singularly Perturbed Differential Equations
This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid
conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ...
Methods for Improving Data-efficiency and Trustworthiness using Natural Language Supervision
Traditional strategies to build machine learning based classification systems employ discrete labels as targets. This limits the usefulness of such systems in two ways. First, the generalizability of these systems is limited ...
Epistasis Detection and Phenotype Prediction in GWAS Using Machine Learning Methods
Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ...
Systems Optimizations for DNN Training and Inference on Accelerated Edge Devices
Deep Neural Networks (DNNs) have had a significant impact on a wide variety of domains, such as Autonomous Vehicles, Smart Cities, and Healthcare, through low-latency inferencing on edge computing devices close to the data ...