Global ETD Search
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Showing 1 to 10 of 10 for “"sparse datasets"”.
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Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms
Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, …
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A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets
… information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern …
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Laboratory investigations of a chaotic flow using braid theory
… on a dense velocity field, techniques based on sparse datasets are increasingly being developed. The braid theory approach to detect Lagrangian coherent structures from sparse sets of trajectories is tested through a periodic, two-dimensional Stokes flow, the rotor-oscillator flow. Combined …
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Latent variable model estimation via collaborative filtering
… show that the estimate converges even for very sparse datasets, which has implications towards sparse graphon estimation. The algorithms can be applied in a variety of settings, such as recommendations for online markets, analysis of social networks, or denoising crowdsourced labels.
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Towards efficacy and efficiency in sparse delay tolerant networks
… approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the …
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Systems Uncertainty in Systems Biology & Gene Function Prediction
… they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological …
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Leveraging large language model embeddings to enhance diversity and mitigate the filter bubble effect in recommender systems
… across three widely-used recommendation datasets from different domains. We further explore how embedding granularity influences performance by generating several sets of embeddings encoding different levels of detail and repeating these experiments. We also assess whether a contrastively …
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Deciphering Leukaemogenic Mechanisms through System-Scale Analysis of Single-Cell RNA Sequencing Data
… of large cell numbers, this is balanced by the sparse and noisy nature of the returned data. Current methods perform poorly on such datasets and either cannot deal with large cell numbers or cannot extract enough relevant signal from sparse count matrices. A new computational tool was designed …
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Implementation of gaussian process models for non-linear system identification
… effective in identifying models from sparse datasets. Therefore, the GP model has been proposed for the identification of models in off-equilibrium regions of operating space, where more established methods might struggle due to a lack of data. The majority of the existing research …
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The resurgence of structure in deep neural networks
… neural network architectures (operating on sparse multimodal and graph-structured data), and a structure-informed learning algorithm for graph neural networks, demonstrating significant outperformance of conventional baseline models and algorithms.