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 20 of 1582 for “"Generative"”.
-
Generative Response.
… this supporting paper, the artist discusses <em>Generative Response</em>, her Master of Fine Arts exhibition. This paper is a narrative of the artist's development, philosophies, and methodologies. Further, it illustrates how her work and development have been affected by studies in humanity, …
-
Towards generative compression
… a class of machine learning models called generative models. These are trained to approximate the true data distribution, and hence can be used to learn an intelligent low-dimensional representation of the data. Using these models, we describe the concept of generative compression and show …
-
Contextualizing generative design
Generative systems have been widely used to produce two- and three-dimensional constructs, in an attempt to escape from our preconceptions and pre-existing spatial language. The challenge is to use this mechanism in real-world architectural contexts in which complexity and constraints imposed by …
-
Generative Modeling with Guarantees
… focusing on improving the reliability of generative models while preserving their flexibility. First, we propose a framework that enables the generation of text conditionally using hard constraints, allowing users to specify certain elements in advance while leaving others open for the …
-
Listening with generative models
… contemporary tools to build and apply rich generative models that describe what we hear. First, I present a hierarchical Bayesian auditory scene synthesis model to address the perceptual organization of sound into sources and events. We aimed to bridge between classical auditory scene …
-
Fine-tuning generative models
Deep generative models have emerged as a powerful modeling paradigm for making sense of large amounts of unlabeled real-world data. In particular, the representations produced by these models have proven to be useful both in improving human understanding of the factors of variation in the original …
-
Generative modelling and adversarial learning
… of real-world data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for deriving generative models from complicated real-world data. GAN and its variants use a generator to synthesise …
-
Generative modelling under epistemic uncertainty
… contribution of this work lies in re-imagining Generative AI under epistemic uncertainty. We introduce Random-Set Large Language Models (RS-LLMs), which predict belief functions over token sets to quantify second-order uncertainty, thereby providing a robust mechanism for hallucination …
-
GTM: the generative topographic mapping
This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis …
-
Probabilistic generative modeling of speech
… modeling. This thesis proposes a probabilistic generative model for speech called the Probabilistic Acoustic Tube (PAT). The highlights of the model are threefold. First, it is among the very first works to build a complete probabilistic model for speech. Second, it has a well-designed model for …
-
Generative Models for Computer Vision
In order to build robust computer vision algorithms, scene models are necessary that are capable of capturing various aspects of the data at the same time. These models should be fairly simple, but capable of adapting to the data. Flexible models, as defined in the machine learning community, are …
-
Exploring knowledge in generative models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
-
Generative modeling of sequential data
… thesis, we investigate various approaches for generative modeling, with a special emphasis on sequential data. Namely, we develop methodologies to deal with issues regarding representation (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), …
-
Belief propagation generative adversarial networks
Generative adversarial networks (GANs) are a class of generative models based on a minimax game. They have led to significant improvement in the field of unsupervised learning, especially image generation. However, most works in GANs are based on learning the distribution of the input dataset …
-
Using generative modelling in healthcare
… We shall also present two popular classes of generative models, namely Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) and their variants. Subsequently, we shall review some new developed imputation methods which are based on GANs and VAEs. We shall assess their …
-
Multimodal generative models for storytelling
… thinking and requires a constant flow of ideas. Generative models have recently gained momentum thanks to their ability to identify complex data's inner structure and learn efficiently from unlabeled data [34]. Natural language generation (NLG) for storytelling is especially challenging because …
-
Generative Discovery via Reinforcement Learning
Discovering new knowledge is crucial for technological advancement and mirrors how humans and animals learn new skills—often through trial and error. Ancient humans, for example, discovered fire by experimenting with different methods, and children learned to walk and use tools through repeated …
-
Score Estimation for Generative Modeling
Recent advances in score-based (diffusion) generative models have achieved state-of-the-art sample quality across standard benchmarks. Building on the remarkable property of these models in estimating scores, this thesis presents three core contributions: 1) new objectives to reduce score …
-
Discriminative, generative, and imitative learning
… three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and …
-
Developing Domain-Specific Generative Methods
Generative AI is a field that is rapidly developing and growing in scale. As research in this area shifts to building on large-scale foundation models and powerful architectures, careful thought has to go into adapting these models to new domains and tasks. The work in this thesis demonstrates …
Page 1 of 80