Browsing by Title
Now showing items 4068-4087 of 8778
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Learning Filters, Filterbanks, Wavelets and Multiscale Representations
The problem of filter design is ubiquitous. Frequency selective filters are used in speech/audio processing, image analysis, convolutional neural networks for tasks such as denoising, deblurring/deconvolution, enhancement, ... -
Learning From Examples Using Hierarchical Counterfactual Expressions
In this study, we develop algorithms for learning concepts from examples. Learning is the capability that allows a system to improve its performance. It involves the ability to correct errors, learn domain knowledge, ... -
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 Invariants for Verification of Programs and Control Systems
Deductive verification techniques in the style of Floyd and Hoare have the potential to give us concise, compositional, and scalable proofs of the correctness of various kinds of software systems like programs and control ... -
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 Non-linear Mappings from Data with Applications to Priority-based Clustering, Prediction, and Detection
With the volume of data generated in today's internet-of-things, learning algorithms to extract and understand the underlying relations between the various attributes of data have gained momentum. This thesis is focused ... -
Learning Robust Support Vector Machine Classifiers With Uncertain Observations
(2015-08-19)The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of ... -
Learning subspace methods using weighted and multi-subspace representations
The learning subspace methods (LSMs) of classification are decision-theoretic pattern recognition methods where the primary model for a class is a linear subspace of the Euclidean pattern space. Classification is based on ... -
Learning to Adapt Policies for uSD card
Machine Learning(ML) for Systems is a new and promising research area where performance of computer systems is optimized using machine learning methods. ML for Systems has outperformed traditional heuristics methods in ... -
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 ... -
Learning Tournament Solutions from Preference-based Multi-Armed Bandits
We consider the dueling bandits problem, a sequential decision task where the goal is to learn to pick `good' arms out of an available pool by actively querying for and observing relative preferences between selected pairs ... -
Learning with Complex Performance Measures : Theory, Algorithms and Applications
(2017-12-07)We consider supervised learning problems, where one is given objects with labels, and the goal is to learn a model that can make accurate predictions on new objects. These problems abound in applications, ranging from ... -
Learning with Multi-domain and Multi-view Graph Data
In many applications, we observe large volumes of data supported on irregular (non-Euclidean) domains. In graph signal processing (GSP) and graph machine learning (GML), data is indexed using the nodes of a graph and ... -
Lectin from mimosa invisa L and its role in rhizobium recognition
Understanding the specificity of the legume-Rhizobium symbiosis may help in improving biological nitrogen fixation in several ways. The causes for this specificity have been studied in only a few systems like clover, ... -
Length Scale Effects in Deformation of Polycrystalline Nickel
(2017-09-20)The demand for compact, efficient and high performance electronic devices and sensor systems has become one of the primary driving force for rapid advancement in miniaturization of current technology. However, the attempt ... -
LES Study Of Free Jets And Jets Impinging On Cuboidal Cavity
(2016-09-15)Numerical solutions based on explicit filtered LES for computing turbulent flow field, of free round jets and impinging round jet on cuboidal cavities, are presented and discussed in this dissertation work. One-parameter ... -
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 ... -
Lessons for Conformal Field Theories from Bootstrap and Holography
(2018-02-06)The work done in this thesis includes an exploration of both the conformal field theory techniques and holographic techniques of the Gauge/Gravity duality. From the field theory, we have analyzed the analytical aspects of ... -
Lessons for Gravity from Entanglement
One of the recent fundamental developments in theoretical high energy physics is the AdS/CFT correspondence [1, 2, 3, 4] which posits a relationship between Quantum Field Theories (QFT) in a given dimension and String ... -
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 ...