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 242 for “"Representation Learning"”.
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Representation learning of recipes
This work 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 -- …
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Representation learning with random images
… To counter these costs, interest has surged in learning from cheaper data sources, such as unlabeled images. In this thesis, we investigate a suite of image generation models that produce images from simple random processes. These are then used as training data for a visual representation …
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Deep Representation Learning for Speaker Recognition
… each user. Over that past few years, deep learning solutions for SR have attracted large amounts of attention. The superior performance of deep learning models in challenging conditions compared to the classical methods, specially in noisy environments and in-the-wild scenarios, have made …
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Scalable subgraph representation learning through simplification
… on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally expensive, and …
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Group representation learning for group recommendation
… we propose and study DeepGroup – a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets, group conditions (e.g., homophily or heterophily), and group decision (or voting) rules. Our …
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Example weighting for deep representation learning
… example weighting is universal in deep learning. Partially arising from the recent work on the risky memorisation behaviours of deep neural networks (Arpit et al., 2017; Zhang et al., 2017b), example weighting becomes an active research filed (Chang et al., 2017; Toneva et al., 2019). …
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Graph Representation Learning for Social Networks
… to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense importance of social network analysis, in this thesis, we aim to …
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Representation learning on heterogeneous spatiotemporal networks
<p>“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical …
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Contrastive representation learning for bioimage quantification
Deep learning has enabled unprecedented progress towards automating the analysis and quantification of large-scale, high-resolution imaging data. However, the majority of current deep learning systems for bioimage analysis is trained with manual annotations, leading to limitations in their …
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Attention-based representation learning on graphs
… becomes more readily available, the field of representation learning has continued to evolve through approaches that seek to describe, understand, and even unify deep learning strategies for data structures such as sets, grids, and graphs. A remarkably successful application of this field of …
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Visual Representation Learning from Synthetic Data
Representation learning is crucial for developing robust vision systems. The effectiveness of this learning process largely depends on the quality and quantity of data. Synthetic data presents unique advantages in terms of flexibility, scalability, and controllability. Recent advances in generative …
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Representation learning for non-sequential data
… 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 data …
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Graph Representation Learning for Drug Discovery
… accelerate this process by developing machine learning (ML) algorithms for three key steps in drug discovery pipeline. First, we develop better property predictors that enable us to effectively navigate known chemical space. The main challenge is to learn a predictor based on a small, biased …
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Interpretable representation learning for visual intelligence
… neural networks in computer vision and machine learning has enabled transformative applications across robotics, healthcare, and security. However, despite the superior performance of the deep neural networks, it remains challenging to understand their inner workings and explain their output …
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Deep Representation Learning on Labeled Graphs
We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more …
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Network Representation Learning with Attributes and Heterogeneity
Network Representation Learning (NRL) aims at learning a low-dimensional latent representation of nodes in a graph while preserving the graph information. The learned representation enables to easily and efficiently perform various machine learning tasks. Graphs are often associated with diverse …
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Exploring the latent geometry for representation learning
L'abstract è presente nell'allegato / the abstract is in the attachment
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Representation learning for long-term collaborative autonomy
… autonomy. In this dissertation, several representation learning approaches are introduced to improve the real-time perception performance of robots in the long-term period. Firstly, I introduce a 3D human skeletal representation learning approach to enable real-time robot awareness of …
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Interpretable belief representation learning on social networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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