Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 27 for “"Probabilistic Graphical Model"”.
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A probabilistic graphical model based data compression architecture for Gaussian sources
… The two aspects of data compression, source modeling, ie. using knowledge about the source, and coding, ie. assigning an output sequence of symbols to each output, are not inherently related, but most existing algorithms mix the two and treat the two as one. This work builds on recent …
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Energy-efficient information inference in wireless sensor networks based on graphical modeling
… proposes a systematic approach, based on a probabilistic graphical model, to infer missing observations in wireless sensor networks (WSNs) for sustaining environmental monitoring. This enables us to effectively address two critical challenges in WSNs: (1) energy-efficient data gathering …
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A Hardware Generator for Factor Graph Applications
… to problems that can be represented as a Probabilistic Graphical Model (PGM). They consist of interconnected variable nodes and factor nodes, which iteratively compute and pass messages to each other. FGs can be applied to solve decoding of forward error correcting codes, Markov chains and …
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Representation learning for non-sequential data
In this thesis, we design and implement new models to learn representations for sets and graphs. Typically, data collections in machine learning problems are structured as arrays or sequences, with sequential relationships between successive elements. Sets and graphs both break this common mold of …
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Understanding freehand diagrams : combining appearance and context for multi-domain sketch recognition
… shifts away from the traditional desktop model (e.g., towards smartphones, tablets, touch-enabled displays), the technology that drives this interaction needs to evolve as well. Wouldn't it be great if we could talk, write, and draw to a computer just like we do with each other? This …
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Spatial and temporal coupling models for the discovery of binding events in ChIP-Seq data
… a complete read generating process under a probabilistic graphical model framework which will determine more accurately binding event locations and enforce alignment of events across conditions. More specifically, we will first propose the so-called Spatial Coupling method which exploits the …
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Example-based grasp adaptation
… between a demo object and new object using probabilistic graphical model. Based on correlation information together with the demo grasp, we generate a grasp for the new object. To ensure that a robot can effectively grasp the object, we adjust the position of grasp contacts until the quality …
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Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks
… and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance …
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
… the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This …
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Modeling the dynamics of nonverbal behavior on interpersonal trust for human-robot interactions
… and validation of a computational model for recognizing interpersonal trust in social interactions. We begin by leverage pre-existing datasets to understand the relationship between synchronous movement, mimicry, and gestural cues with trust. We found that although synchronous …
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Semi-supervised and active training of conditional random fields for activity recognition
… However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and …
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Aircraft state estimation using cameras and passive radar
… (PF) to handle the multiple targets and adds a probabilistic graphical model (PGM) data association stage to compute the mapping from detections to trackers. The MTTF was applied to the problem of passively monitoring airspace. Two applications were built: a passive radar MTT module and a …
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UNDERSTANDING DIFFERENTIAL ABUNDANCE IN MICROBIAL ECOLOGY USING COMMUNITY STRUCTURE.
… a novel framework that reframes DAA through probabilistic graphical model inference, integrating network analysis with abundance profiling to uncover not only key microbial features but also the intricate interactions within their communities. Central to this work is the development of …
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Mathematical and Computational Foundations to Enable Predictive Digital Twins at Scale
A digital twin is a computational model that evolves over time to persistently represent a unique physical asset. Digital twins underpin intelligent automation by enabling asset-specific analysis and data-driven decision-making. Although the promise of digital twins is well established, …
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Structured video content analysis : learning spatio-temporal and multimodal structures
… hierarchical sequence summarization, is a probabilistic graphical model that learns spatio-temporal structure of human action in a fine-to-coarse manner. It constructs a hierarchical representation of video by iteratively summarizing the video sequence, and uses the representation to learn …
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Learning to understand spatial language for robotic navigation and mobile manipulation
… semantic map of the environment together with a model of contextual relationships between objects to infer this plan, which finds the query object with minimal travel time. The contextual relationships are learned from the captions of a large dataset of photos downloaded from Flickr. Simulated …
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Listas KBM2L para la síntesis de conocimiento en sistemas de ayuda a la decisión
… and Influence Diagrams, among other reasoning models, imply the use of tables with diversified information. Among them we focus on the conditional probability tables that represent the probabilistic relationships among variables and the tables of the optimal decisions resulting from the model …
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Knowledge discovery with recommenders for big data management in science and engineering communities
… that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) …
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Algorithms for analyzing complex structural variations in cancer genomes
… SVs with base-pair resolution and applies a probabilistic graphical model to simultaneously quantify allele specific copy number of SVs (ASCNS) and genomic regions (ASCNG). Through evaluation on simulated datasets with different parameter settings, Weaver was demonstrated to be highly …
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Systems, models and algorithms for failure diagnosis in networked infrastructure
… in designing such RCA solutions: (1) accurately modeling the behavior of the system and (2) using the model to infer the root causes accurately. Existing works on RCA either (1) fall short in modeling the complexities of an environment and hence utilize imprecise models or (2) utilize an …
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