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Browsing Cornell University Graduate School by Subject "machine learning"
Now showing items 1-20 of 45
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Active Perception and Planning for Modular Self-Reconfigurable Robots
(2018-08-30)Modular robots have the unique ability to reconfigure their shape and capabilities to adapt to various challenges in the environment. In order to perform tasks autonomously in unknown environments, active perception and ... -
Adaptive Learning: Algorithms and Complexity
(2019-05-30)Recent empirical success in machine learning has led to major breakthroughs in application domains including computer vision, robotics, and natural language processing. There is a chasm between theory and practice here. ... -
Analyzing Life-logging Image Sequences
(2017-01-30)With the abundance of ubiquitous cameras, it has become easier people take pictures of everything and everywhere. People take pictures of their possessions, interesting subjects and the places they visit. There is a class ... -
Automated analysis of quantitative image biomarkers from low-dose chest CT scans
(2018-08-30)A quantitative imaging biomarker is a quantitatively measured characteristic derived from medical images, which serves as cost¬-effective and non¬invasive tools for patient health assessment, including diagnosis and periodic ... -
Automatic Patent Classification Using Support Vector Machines And Its Applications
(2011-08-31)Patents as an important component belonging to innovation can serve as an index in representing the technological development level in a given industry. However, agricultural biotechnology patent (ABP) data have not been ... -
Bayesian Optimization with Parallel Function Evaluations and Multiple Information Sources: Methodology with Applications in Biochemistry, Aerospace Engineering, and Machine Learning
(2017-01-30)Bayesian optimization, a framework for global optimization of expensive-to-evaluate functions, has recently gained popularity in machine learning and global optimization because it can find good feasible points with few ... -
Coordinated Static and Dynamic Scheduling for High-Quality High-Level Synthesis
(2019-05-30)The breakdown of Dennard scaling has led to the rapid growth of specialized hardware accelerators to meet ever more stringent performance and energy requirements. However, great performance-per-watt comes at the cost of ... -
Counterfactual Evaluation And Learning From Logged User Feedback
(2017-05-30)Interactive systems that interact with and learn from user behavior are ubiquitous today. Machine learning algorithms are core components of such systems. In this thesis, we will study how we can re-use logged user behavior ... -
Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks
(2018-08-30)Fully automatic processing of images is a key challenge for the 21st century. Our processing needs lie beyond just organizing photos by date and location. We need image analysis tools that can reason about photos like a ... -
Deep Sequential and Structural Neural Models of Compositionality
(2017-01-30)Recent advances in deep learning have provided fruitful applications for natural language processing (NLP) tasks. One key advance was the invention of word vectors, representing every word in a dense, low-dimensional vector ... -
Deformable Media for Visual and Tactile Interfaces
(2017-05-30)We experience a variety of natural touch surfaces in our daily lives. These surfaces range in compliance from hard to soft, and in texture from smooth to rough. Human computer interfaces, on the other hand, have largely ... -
Detecting Common Objects in Context
(2017-08-30)Visual scene understanding is a basic function of human perception and one of the primary goals of computer vision. Object detection, which involves recognizing and localizing objects present in an environment, is a ... -
DFT study of the complex diffusion of oxygen in cobalt & Machine learning of ab-initio energy landscapes for crystal structure predictions
(2019-05-30)Point defects in solids are important because they can have a large influence on the mechanical, electronic, and optical properties. One of the most ubiquitous defects in metals is oxygen. Here, we use DFT to show that all ... -
EMPIRICAL METHODS FOR FINE-GRAINED OPINION EXTRACTION FROM TEXT
(2008-07-29)Opinions are everywhere. The op/ed pages of newspapers, political blogs, and consumer websites like epinions.com are just some examples of the textual opinions available to readers. And there are many consumers who are ... -
Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval
(2012-05-27)Music exemplifies the repetitive patterns in nature. These patterns lend a distinctiveness to sound sources that make them identifiable. In audio analysis, this information can be accessed by using a process called spectral ... -
Forecasting Hotel Demand using Machine Learning Approaches
(2019-08-30)A critical aspect of revenue management is a firm's ability to predict future demand. Historically hotels have used pick-up based models owing to the complexities of trying to build casual models of demands. Machine ... -
Improving Machine Learning Approaches to Noun Phrase Coreference Resolution
(2004-07-16)Human speakers generally have no difficulty in determining which noun phrases in a text or dialogue refer to the same real-world entity. This task of identifying co-referring noun phrases --- noun phrase coreference ... -
Improving Machine Learning Beyond the Algorithm
(2018-08-30)In interactive machine learning systems (IMLSs), such as search engines, social networks, and e-commerce sites, machine learning algorithms and user interfaces are inseparably linked. My thesis demonstrates that improving ... -
Interpretable Approaches to Opening Up Black-Box Models
(2019-08-30)In critical domains such as healthcare, finance, and criminal justice, merely knowing what was predicted, and not why, may be insufficient to deploy a machine learning model. This dissertation proposes new methods to open ... -
Inverse Problem In Quantitative Susceptibility Mapping: Numerical And Machine Learning Approaches
(2019-05-30)Magnetic susceptibility reflects the concentration of bio-metal elements such as iron, calcium or gadolinium, providing means to investigate diseases such as multiple sclerosis, Alzheimer’s disease, hemorrhage and ...
