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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 ...
Predictive Motion Planning for Safe and Efficient Autonomous Driving
The advent of Autonomous Vehicles (AVs) has the potential to revolutionize transportation systems, promising significant improvements in safety, efficiency, and passenger comforts. Safety, the cornerstone of AVs, demands ...
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 ...
On the Optimality of Generative Adversarial Networks — A Variational Perspective
Generative adversarial networks (GANs) are a popular learning framework to model the underlying distribution of images. GANs comprise a min-max game between the generator and the discriminator. While the generator transforms ...
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 ...