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.
Results
Showing 1 to 6 of 6 for “"Neural Embeddings"”.
-
RGB-D Likelihood for 3D Inverse Graphics
… and real-world data. We propose a novel 3D Neural Embedding Likelihood (3DNEL) over RGB-D images to address this gap. 3DNEL uses neural embeddings to predict 2D-3D correspondences from RGB and combines this with depth in a principled manner. 3DNEL is trained entirely from synthetic images …
-
Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems
… oversampling techniques, and (2) using neural embeddings to improve feature representation for anomaly detection. Together, these contributions offer a methodology for evaluating and improving anomaly detection pipelines in domains where rare, high-impact events must be detected while …
-
Comparative evaluation of conventional search architectures and large language model-based approaches
… transformer (ChatGPT) with browsing, employ neural embeddings and generative reasoning to deliver contextualised and conversational outputs. While these advances enhance informational synthesis, they also introduce challenges such as hallucinations, opaque reasoning processes, and reduced …
-
Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings
… we construct 3 sets of 50-dimensional document embeddings to represent the 1,917 recorded instances of challenging behaviors demonstrated in Applied Behavior Analysis therapy. These embeddings are learned through three processes: a TF-IDF weighted sum of Word2Vec embeddings, Doc2Vec embeddings …
-
Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making
… addressing the challenge of optimizing trained neural networks for data-driven decision-making. Although neural networks can encode rich representations of preferences or outcomes, directly optimizing their outputs can be computationally intractable and often may produce unrealistic …
-
A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data
<p>Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents …