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 11 of 11 for “"Latent spaces"”.
-
Parametric PAINTOVER: Generating Design Models via Image Encoders and Latent Trajectories
… models are not cyclic, their input and output spaces are not interchangeable without human intervention. Models must be reconfigured to accommodate out-of-domain changes, preventing parametric design tools from being integrated into early phases of design where changes are commonplace. We …
-
Optimal anticipatory control as a theory of motor preparation
… of the cortex then evolves from these optimal subspaces, producing patterns of neural activity that serve as control inputs to the musculature. This theory, however, does not address the following questions: what characterizes the optimal subspace and what are the neural mechanisms that underlie …
-
Advance metabolite identification from tandem mass spectra using deep generative models
… map metabolite structures and MS/MS spectra to latent spaces separately. Then, we train a classifier to identify real metabolite-spectrum matches based on the latent space features of metabolites and spectra. Further, we build a generative adversarial network (GAN) to optimize the classifier as …
-
Innovations in Urban Computing: Uncertainty Quantification, Data Fusion, and Generative Urban Design
… images as the imagery inputs. DHM can construct latent spaces that significantly outperform classical demand and deep learning models in predicting aggregate and disaggregate travel behavior. Such latent spaces can also be used to generate new satellite images that do not exist in reality and …
-
Generative Bayesian Optimization for Structured Design
… discrete domains such as molecules or proteins. Latent space Bayesian optimization (LS-BO) is a newly emerging approach for this setting. LS-BO employs generative models to embed discrete, structured inputs into continuous latent spaces where BO techniques can be applied. This dissertation …
-
Interpretability of Neural Networks Latent Representations
… output of these models, leaving their internal latent representations unexplored. In this thesis, we describe how interpretability research has recently expanded its scope beyond the output of neural networks. We focus on 3 types of interpretability methods: feature importance, example-based and …
-
Computational Approaches to Facilitate Automated Interchange between Music and Art
… to produce a pure computational approach, where latent spaces of two trained variational autoencoder networks are interchanged. Finally, the earlier approaches are merged to explore human encoded metrics mapped to a generative model to interpolate music synthesis. For all approaches, results are …
-
Structural design synthesis using machine learning
… of a fluid design process. Second, the design spaces used to generate and evaluate design variations are so vast that they are virtually impossible for humans to effectively explore. Finally, due to the intrinsically human nature of architecture and design, there is strong resistance to any …
-
PROCESS MINING FOR REENGINEERING CIRCULAR AND RESILIENT PRODUCTION PROCESSES
… and semantic rules from process data into a latent space is a key step for training machine learning models. Usually, most proposals in the literature rely on naive techniques to encode categorical attributes from events, such as the well-known one-hot encoding. Furthermore, a second level of …
-
Computational and Statistical Detection of High-Dimensional Latent Space Structure in Random Networks
A probabilistic latent space graph PLSG (n, Ω, D, σ) is parametrized by its number of vertices n, a probability distribution D over some latent space Omega, and a connection function [mathematical function] such that [mathematical formula] almost surely with respect to D. To sample from …
-
Improved integration of information to reduce subsurface model bias
… geometry methods to stabilize lower dimensional spaces for uncertainty quantification and interpretation. Finally, I create a methodology to assess, evaluate, and interpret the stability of deep learning latent feature spaces. These novel methodologies demonstrate the importance of improved …