Browsing by Title
Now showing items 3054-3073 of 6421
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A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications
Deep neural networks are the most widely used examples of sophisticated mapping functions from feature space to class labels. In the recent years, several high impact decisions in domains such as finance, healthcare, law ... -
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 Algorithms Using Chance-Constrained Programs
(2010-07-08)This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with ... -
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 Decentralized Goal-Based Vector Quantization
(2012-05-04) -
Learning Dynamic Prices In Electronic Markets
(2011-04-19) -
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 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 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 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 ... -
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 ... -
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 ... -
Leveraging Resources For Strategic Organizational Renewal A Co-Evolutionary Perspective
(2010-07-23)Multiple strategic discontinuities of the constantly changing business environment are driving organizations, both large and small to seek new ways of conducting business to create wealth. The only way organizations can ...