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 23 for “"vector representations"”.
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One-vector representations of stochastic signals for pattern recognition
… Often, a stochastic signal is ideally of a one-vector form so that it appears as a single data point in a possibly high-dimensional representational space, as the majority of pattern recognition algorithms by design handle stochastic signals having a one-vector representation. More importantly, …
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Combining raster- and vector-representations for image and geometry processing applications
… thesis we show that a combination of raster- and vector-representations of the geometric information contained in the input data provides novel opportunities and ways for solving very challenging tasks in the areas of image and geometry processing. By this we also draw parallels between these two, …
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A vector space approach for aspect-based sentiment analysis
Vector representations for language have been shown to be useful in a number of Natural Language Processing (NLP) tasks. In this thesis, we aim to investigate the effectiveness of word vector representations for the research problem of Aspect-Based Sentiment Analysis (ABSA), which attempts to …
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NOVEL DATA MINING ALGORITHMS FOR ANALYSIS OF ELECTRONIC HEALTH RECORDS
… to convert natural text from medical notes to vector representations suitable for deep learning algorithms, (2) how to help healthcare researchers select a patient cohort from EHRs, and (3) how to use EHRs to identify patient diagnoses and treatments. In the first part of the thesis, we present …
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Representation learning of recipes
… introduces methods for learning distributed, vector representations of cooking recipes. The individual components of a recipe -- the images, instructions, and ingredients -- are first treated individually. These representations are learned from a large, multi-modal dataset collected -- and …
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Learning sentiment and semantic relatedness in user generated content using neural models
… to address these tasks. The model uses the word vector representations generated using word2vec and computes the convolutional vectors of the user-generated reviews. These vectors are then employed to predict the aspect categories and their corresponding sentiments. We evaluate the performance of …
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AWE: Attention Word Embedding
Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the …
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Food adulteration detection using neural networks
… the use of recurrent neural networks to generate vector representations of ingredients from Wikipedia text and make predictions. Finally, we use these representations to develop a sequential method that has the capability to improve prediction accuracy as new observations are introduced. The …
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Neural network architectures for Prepositional Phrase attachment disambiguation
… defined using a recursive neural network. Word vector representations are obtained from large amounts of raw text and fed into the neural network. The vectors are first forward propagated up the network in order to create a composite representation, which is used to score all possible …
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Multi-theme sentiment analysis with sentiment shifting
… sentiment by learning embeddings (i.e., vector representations) for both themes and words, and derives the shifter effect learning algorithm by modeling the shifted sentiment in a logistic regression model. Extensive experiments have been conducted on Yelp business reviews and IMDB movie …
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Efficient Algorithms for Vector Similarities
… cog in machine learning is the humble embedding: vector representations of real world objects such as text, images, graphs, or molecules whose geometric similarities capture intuitive notions of semantic similarities. It is thus common to curate massive datasets of embeddings by inferencing on a …
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Classification of computer programs in the Scratch online community
… Natural Language Processing (NLP) techniques to vectorize and classify Scratch projects by type. This effort included constructing a labeled dataset of 873 Scratch projects and their corresponding types, to be used for training a supervised classifier model. This dataset was constructed through a …
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Increasing Accessibility of Electronic Theses and Dissertations (ETDs) Through Chapter-level Classification
… word and document embeddings to generate better vector representations of this corpus. It also describes a methodology to leverage extractive summaries of chapters of an ETD to aid in the classification process. Our findings indicate that custom embeddings and the use of summarization techniques …
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Inverse Constitutional AI
… involves embedding preference pairs into vector representations, clustering the embeddings to group related preferences, generating interpretable principles for each cluster using language models, and validating these principles against held-out samples. Empirical evaluation is conducted …
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AI-driven smart cities: Digital twin simulation, V2X communication, and EV infrastructure optimization
… optimization, leveraging embedding vector representations, matrix factorization, and clustering methods. By analyzing real-world EV charging station data, the study uncovers key utilization patterns, proposes location optimization strategies, and introduces Non-Intrusive Load …
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Deep learning for spoken dialogue systems : application to nutrition
… architecture learns a shared latent space, where vector representations of natural language queries lie close to embeddings of database entries that have semantically similar meanings. The first instantiation of this technology is in the nutrition domain, with the goal of reducing the burden on …
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REPRESENTATION LEARNING FOR VISUAL TASKS: A STUDY OF ATTENTION AND INFORMATION SELECTION
… context, developing efficient multi-vector representations, and explicitly controlling attention. The research utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) and targets improvements in image classification (single and …
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Injecting Inductive Biases into Distributed Representations of Text
Distributed real-valued vector representations of text (a.k.a. embeddings), learned by neural networks, encode various (linguistic) knowledge. To encode this knowledge into the embeddings the common approach is to train a large neural network on large corpora. There is, however, a growing concern …
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Predicting unknown adverse drug reactions using an unsupervised node embedding algorithm
… an unsupervised algorithm to create embeddings (vector representations) of the nodes in the knowledge graph, and finally runs the prediction task. The framework enables an embedding to be learned for any newly added node as long as it is connected with the other nodes, and users can create …
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Explainable AI framework through Multi-Context Multi-Dimensional Graph Neural Network
… These relationships were converted into dense vector representations, capturing the nuanced interrelations of the graph constituents. This venture provided profound insights into the fabric of digital communication, steering in a groundbreaking method to identify community structures within …
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