Browsing Department of Computational and Data Sciences (CDS) by Title
Now showing items 56-75 of 116
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Integrating Coarse Semantic Information with Deep Image Representations for Object Localization, Model Generalization and Efficient Training
Coarse semantic features are abstract descriptors capturing broad semantic information in an image, including scene labels, crude contextual relationships between objects in the scene, or even objects described using ... -
Intelligent Methods for Cloud Workload Orchestration in Data Centers
Cloud workload orchestration plays a pivotal role in optimizing the performance, resource utilization, and cost effectiveness of applications in data centers. As modern businesses and IT operations are migrating their ... -
Intelligent Orchestration of Autonomous Systems Across Edge-Cloud Continuum
The benefits of autonomous mobile platforms, such as Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras, are enhanced by compact edge accelerators that are co-located, such as the NVIDIA Jetson with 100s of CUDA ... -
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 ... -
Investigation of the Indian Summer Monsoon Rainfall Using Statistical and Machine Learning Techniques
The Indian Summer Monsoon is an important atmospheric phenomenon, marked by a characteristic seasonal wind reversal pattern, delivering 70 to 90% of the annual rainfall to the Indian subcontinent. Monsoon rain profoundly ... -
Landmark Estimation and Image Synthesis Guidance using Self-Supervised Networks
The exponential rise in the availability of data over the past decade has fuelled research in deep learning. While supervised deep learning models achieve near-human performance using annotated data, it comes with an ... -
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
With rapid development in technology and ubiquitous presence of diverse types of sensors, a large amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/ object/event has ... -
Learning Compact Architectures for Deep Neural Networks
(2018-05-22)Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just ... -
Learning from Limited and Imperfect Data
Deep Neural Networks have demonstrated orders of magnitude improvement in capabilities over the years after AlexNet won the ImageNet challenge in 2012. One of the major reasons for this success is the availability of ... -
Learning Multiple Initial Conditions using Physics Informed Neural Networks
Physics-Informed Neural Networks (PINNs) and its variants have emerged as a tool for solving differential equations in the past few years. Although several variants of PINNs have been proposed, the majority of these ... -
Learning to Perceive Humans From Appearance and Pose
Analyzing humans and their activities takes a central role in computer vision. This requires machine learning models to encapsulate both the diverse poses and appearances exhibited by humans. Estimating the 3D poses of ... -
Lesion Synthesis using Physics-Based Noise Models for Low-Data Medical Imaging Regime applications
Lesion segmentation and their progression prediction in medical imaging relies critically on the availability of manually annotated, heterogeneous large pathological datasets. Acquiring such diverse large datasets is also ... -
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 ... -
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 ... -
Migrating VM Workloads to Containers: Issues and Challenges
Modern day enterprises are adopting virtualization to leverage the benefits of improved server utilization through workload consolidation. Server consolidation provides this benefit to enterprise applications as many of ... -
Mitigating Domain Shift via Self-training in Single and Multi-target Unsupervised Domain Adaptation
Though deep learning has achieved significant successes in many computer vision tasks, the state-of-the-art approaches rely on the availability of a large amount of labeled data for supervision, collection of which is ... -
Modeling physiological transport at scales: connecting cells to organs
The physiological system is a complex network in which each organ forms a subsystem, and the functional networks in different subsystems communicate to maintain the body’s overall homeostasis. The ability to simultaneously ... -
Novel Deep Learning Methods for Improving Low-Dose Computed Tomography Perfusion Imaging of Brain
Computed Tomography (CT) Perfusion imaging is a non-invasive medical imaging modality that has also established itself as a fast and economical imaging modality for diagnosing cerebrovascular diseases such as acute ischemia, ... -
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 ... -
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 ...