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 24 for “"Structured Prediction"”.
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Structured prediction with indirect supervision
Structured tasks, which often involve many interdependent decisions for each example, are the backbone for many important applications such as natural language processing tasks. The models built for structured tasks need to be capable of assigning values to a set of interdependent variables. In …
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Exploring the impacts of organisational change : a structured prediction approach
This was a qualitative, single case study that investigated the operational and relational dynamics of a multifaceted change process within a large Australian company. The Company had an extremely complex organisation and supply-chain structure and often its entities were severely impacted by …
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Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference
Structured prediction tasks involve an inference step which allows for producing coherent label assignments to the output structure. This can be achieved by constraining the output using prior knowledge about the domain. This paradigm is called Constrained Conditional Models; and it involves …
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Crosslingual Sharing for Low-Resource Natural Language Processing
… of tasks, including language modeling and structured prediction problems such as syntactic and semantic parsing. This is due in large part to the use of supervised neural networks and more recently to unsupervised contextualized representations. However, these techniques rely on resources, …
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Co-generation with GANs using AIS based HMC
… classical ways of modeling distributions like structured prediction. In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs). Therefore, in this paper, we …
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Towards structured representation of academic search results
… scholarly search engines produce lengthy unstructured result lists, which are difficult to comprehend, interpret and browse.An informative visual summary could convey useful information about the returned results as a whole, without the need to sift through individual publications.The first …
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Coactive Learning Algorithms for Constructive Preference Elicitation
… elicitation framework is based on online structured prediction, a machine learning technique that deals with sequential decision problems over structured objects. By combining online structured prediction and state-of-the-art constraint solvers we can efficiently learn user utility models …
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Algorithms for structural learning with decompositions
Structured prediction describes problems which involve predicting multiple output variables with expressive and complex interdependencies and constraints. Learning over expressive structures (called structural learning) is usually time-consuming as exploring the structured space can be an …
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Advances in discriminative dependency parsing
… parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a; Carreras, 2007; Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and max-margin models …
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Modeling Structured Data with Invertible Generative Models
… Glow (c-Glow), a conditional generative flow for structured output learning, which is an advanced variant of supervised learning with structured labels. Traditional structured prediction models try to learn a conditional likelihood, i.e., p(y|x), to capture the relationship between the structured …
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Zero-Shot Scene Graph Relationship Prediction using VLMs
Scene Graph Relationship Prediction aims to predict the interaction between the objects in an image. Despite the recent surge of interest in open-vocabulary and zero-shot SGG, most approaches still require some form of training or adaptation on the target dataset, even when using Vision-Language …
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Contributions to the Computational Treatment of Non-literal Language
… data in capturing formulaic MWEs using structured prediction. We also develop a novel neural architecture to speci fically address the issue of discontinuous MWEs using a combination of Graph Convolutional Neural Networks (GCNs) and self-attention. The proposed model is subsequently …
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Learning with Degree-Based Subgraph Estimation
… using strategies similar to maximum-margin structured prediction. The main methods and results in this thesis represent a deep exploration of exact degree-based estimation for machine learning from network data, and furthermore lead to various extensions and applications of the main idea …
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A tree-to-tree model for statistical machine translation
… problem. In this thesis, we use a linear structured prediction model to solve this learning problem. A human evaluation of the AEP-based translation approach in a German-to-English task shows significant improvements in the grammaticality of translations. This thesis also presents a …
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Learning with structured decision constraints
This thesis addresses several prediction and estimation problems under structured decision constraints. We consider them in two parts below. Part 1 focuses on supervised learning problems with constrained output spaces. We approach it in two ways. First, we consider an algorithmic framework that is …
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Essays on collective intelligence
… second essay, we characterize a class of semi-structured prediction problems, where patterns are difficult to discern, data are difficult to quantify, and changes occur unexpectedly. Making correct predictions under these conditions can be extremely difficult, and is often associated with high …
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Learning to Solve Optimization Problems with Hidden Components: Applications in Automated Treatment Planning
… Inverse Linear Optimization (ILO), which is a structured prediction problem for estimating the cost vector of a linear program from observed decisions. Our framework specializes to existing variants in the literature, admits new solution algorithms, and includes a statistical goodness-of-fit …
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Indoor place classification for intelligent mobile systems
… office and corridor, in both independent and structured prediction ways, have been proposed in this research. The solution modelling dependencies between random variables, which takes the spatial relationship between observations into consideration, is further extended by integrating the …
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Online Structured Learning for Real-Time Computer Vision Gaming Applications
… in a more principled way through the use of structured learning. As well as providing a more powerful framework for adaptive visual object tracking, our approach also outperforms state-of-the-art tracking algorithms on standard datasets. Next we consider the task of keypoint-based object …
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Data-Centric Machine Learning for Speech and Audio
… an end-to-end neural ASR model, which performs a structured prediction task. We also demonstrate how data valuation can be used for environmental sound classification. In the context of ecoacoustic monitoring of large data with limited labels, we estimate Shapley values of audio clips with …
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