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Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems
Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ...
Towards Learning Adversarially Robust Deep Learning Models
Deep learning models have shown impressive performance across a wide spectrum of computer vision
applications, including medical diagnosis and autonomous driving. One of the major concerns that
these models face is their ...
Scalability Bottleneck Analysis of High Performance Applications
Obtaining high performance and scalability for high performance applications are challenging.
There are various bottlenecks including, higher rate of memory access, complex algorithm, high
rate of communication, big ...
Improving Data Center Utilisation by Reducing Fragmentation
Virtualization enables better server consolidation and utilisation compared to stand-alone
servers running a single workload. This enabled wide-spread cloud adoption among many
organizations. Data center utilisation is ...
Deep Learning for Hand-drawn Sketches: Analysis, Synthesis and Cognitive Process Models
Deep Learning-based object category understanding is an important and active area of research
in Computer Vision. Most work in this area has predominantly focused on the portion of
depiction spectrum consisting of ...
Stabilized finite element schemes for computations of viscoelastic free-surface and two-phase flows
Viscoelastic flows can be found in a wide range of industrial and commercial applications
such as enhanced oil recovery, pesticide deposition, medicinal/pharmaceutical sprays, drug
delivery, injection molding, polymer ...
Static Analysis and Dynamic Monitoring of Program Flow on REDEFINE Manycore Processor
Manycore heterogeneous architectures are becoming the promising choice for high-performance computing applications. Multiple parallel tasks run concurrently across different processor cores sharing the same communication ...
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 ...
Novel Neural Architectures based on Recurrent Connections and Symmetric Filters for Visual Processing
Artificial Neural Networks (ANN) have been very successful due to their ability to extract meaningful information without any need for pre-processing raw data. First artificial neural networks were created in essence to ...
Assessing protein contribution to phenotypic change using short, coarse grained molecular dynamics simulations
Understanding the functional mapping between genotype and phenotype is an important problem that has ramifications for various diseases. Various existing computational methods can infer these disease-related functional ...