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Leveraging KG Embeddings for Knowledge Graph Question Answering
Knowledge graphs (KG) are multi-relational graphs consisting of entities as nodes and relations
among them as typed edges. The goal of knowledge graph question answering (KGQA) is to
answer natural language queries posed ...
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 ...
Communication Overlapping Krylov Subspace Methods for Distributed Memory Systems
Many high performance computing applications in computational fluid dynamics, electromagnetics etc. need to solve a linear system of equations $Ax=b$. For linear systems where $A$ is generally large and sparse, Krylov ...
INTERPIN: identifying INtrinsic transcription TERminators, hairPINs in bacteria
The conversion of DNA to RNA through transcription is an important step in the life cycle of every organism. It ensures that the genetic information in DNA is converted through RNA into instructions/blueprints for the ...
Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud
Modern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments ...
Constrained Stochastic Differential Equations on Smooth Manifolds.
Dynamical systems with uncertain fluctuations are usually modelled using Stochastic Differential Equations (SDEs). Due to operation and performance related conditions, these equations may also need to satisfy the constraint ...
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 ...
Development of Novel Deep Learning Models with Improved Generalizability for Medical Image Analysis
Medical imaging is a process of visualization of disease/tissue in a non-invasive manner. Several imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and ...
Sparsification of Reaction-Diffusion Dynamical Systems on Complex Networks
Graph sparsification is an area of interest in computer science and applied mathematics. Spar-
sification of a graph, in general, aims to reduce the number of edges in the network while
preserving specific properties of ...
End-to-end Resiliency Analysis Framework for Cloud Storage Services
Cloud storage service brought the idea of a global scale storage system available on-demand and accessible from anywhere. Despite the benefits, resiliency remains one of the key issues that hinder the wide adaptation of ...