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Hyper-optimized Machine Learning and Deep Learning Methods For Geo-spatial and Temporal Function Estimation
(2018-08-13)
Owing to a high degree of freedom in human mobility, accurate modelling/estimation of human mobility function remains a challenge. Numerous work in the literature have tried to address the challenge using various traditional ...
Divide and Conquer Approach to Scalable Substructure Discovery: Partitioning Schemes, Algorithms, Optimization and Performance Analysis using Map/Reduce Paradigm
(2017-05-08)
With the proliferation of applications rich in relationships, graphs are becoming the preferred choice of data model for representing/storing data with relationships. The notion of "information retrieval'' and "information ...
Deep Representation Learning for Clustering and Domain Adaptation
(2019-12-05)
Representation learning is a fundamental task in the area of machine learning which can significantly influence the performance of the algorithms used in various applications. The main goal of this task is to capture the ...
Optimizing Resource Utilization, Efficiency and Scalability in Deep Learning Systems
(2023-05-01)
This thesis addresses the challenges of utilization, efficiency, and scalability faced by deep learning systems, which are essential for high-performance training and serving of deep learning models. Deep learning systems ...