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 48 for “"vae"”.
-
Synonymous question generation: Learning to ask in different ways using variational autoencoders
… autoencoder-based model, the Template VAE (T-VAE). Evaluating the generated questions via the Fre'chet InferSent Distance (FID) and the Multiset-Jaccard-k-gram (MS-Jaccard-k) Measure, two joint diversity-quality metrics, demonstrates that the proposed model is able to produce fluent …
-
Deep Generative Models and Biological Applications
… recent proposed Variational auto-encoders (VAE) framework is an efficient high-dimensional inference method to modeling complicated data manifold in an approximate Bayesian way, i.e., variational inference. </p><p>We first discuss how to design fast stochastic backpropagation algorithm for …
-
Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation
… images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have …
-
JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback
Recently, Variational Autoencoders (VAEs) have shown remarkable performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. However, existing VAE-based recommendation models learn …
-
Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks
… First, we train a variational autoencoder (VAE) using the model updates collected from the edge servers. This allows the VAE to discern between benign and adversarial model updates. Following that, we deploy the VAE to detect which edge servers at the cohort level contain malicious clients. …
-
Leveraging the Latent Space for Model Understanding and Optimization
… to models in image generation such as the VQ-VAE which often struggle to reconstruct images capturing the finer details of the original input image. By introducing lightweight and straightforward modifications to the VQ-VAE’s loss function and dictionary selection process, we enable the …
-
Missing data imputation in a clinical registry with deep generative models
… Machine (RBM) and Variational Autoencoder (VAE) as potential modeling and imputation techniques for missing data. We examined the training of the model with incomplete dataset and mixed types of variable. For VAE, we further discussed a robust and efficient Markov Chain Monte Carlo (MCMC) …
-
Unsupervised learning of disentangled representations for speech with neural variational inference models
… an existing variational autoencoder (VAE) model for learning latent representations, and derive novel latent space operations for speech transformation. The transformation method is applied to unsupervised domain adaptation problems, which addresses the transferability issues of …
-
Tackling Key Challenges to Guide Clinical Decisions in Cardiovascular Diseases
… contrastive Variational Autoencoder (contrastive-VAE), an approach that models both the majority and minority classes as having shared latent properties, to address the following challenges: 1) Predicting rare adverse clinical outcomes after ACS; 2) Quantifying common support for estimating the …
-
Enhancing Microbiome Host Disease Prediction with Variational Autoencoders
… reduction and variational autoencoders (VAE) in generating synthetic microbiome profiles as a potential method to deal with this issue and increase existing disease classification model performance. Results are compared across various baseline machine learning models with traditional …
-
Injecting Inductive Biases into Distributed Representations of Text
… these properties with Variational Autoencoders (VAEs). First, we regulate the amount of information encoded in a sentence embedding via constraint optimisation of a VAE objective function. We show that increasing amount of information allows to better discriminate sentences. Afterwards, to impose …
-
Towards Learning the Geometry of Data: From Diffusion Models to Riemannian Geometry
… models. In chapter 6, we introduce ScoreVAE, a novel Variational Auto-encoder (VAE) that alleviates the typical VAE limitation of blurry reconstructions by combining a frozen pretrained diffusion model with a learnable time-dependent encoder to model the reconstruction distribution. In …
-
Robust and interpretable high-dimensional machine learning for predictive cancer medicine
… upon extensions to the variational autoencoder (VAE), a probabilistic latent variable model that leverages neural networks to learn latent representations. Specifically, I propose a VAE variant that generates latent representations which explicitly capture genetic dependencies in cancers. I …
-
Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells
… causal model into a variational autoencoder (VAE), the framework generates detailed and comprehensive transcriptomic responses while maintaining the capacity to handle noisy, large-scale single-cell data. Two deep variational architectures are explored within this framework, corresponding to …
-
Explainable Machine Learning Prediction of Antimicrobial Peptide Targeting Streptococcus mutans
… a pipeline that uses variational autoencoder (VAE) sampling, genetic algorithm (GA) optimization, and guided decoding to generate new peptide candidates. Our ML design combines the distinct phases through VAE sampling which creates a continuous design space, GA optimization enables to search by …
-
Spatiaalinen multiomiikka ja syväoppiminen: Datan haasteet ja menetelmäkehitys
… (variational autoencoder, VAE), graafipohjaiset neuroverkot (graph neural network, GNN) sekä hybridimallit, jotka voivat yhdistellä useampaa eri arkkitehtuuria yhdeksi kokonaisuudeksi. Kirjallisuuskatsauksessa käsiteltyjen tutkimusten pohjalta on selvää, että syväoppimisen …
-
Künstliche Intelligenz und Recht in den GCC-Staaten
… zur DSGVO, wenngleich insbesondere in den VAE ein stärker einwilligungszentrierter Ansatz verfolgt wird. Abschließend wird der regulatorische Umgang mit Hochrisiko-KI-Systemen analysiert, exemplarisch am Überwachungssystem „Oyoon“. Insgesamt zeigt die Untersuchung ein Spannungsverhältnis …
-
Generative models for predictive UI design tools
… (GANs) [7] and variational auto-encoders (VAEs) [15]. We train the GAN and VAE models over 1949 mobile UIs that represent a variety of screen types (e.g. Login, Onboarding), and compare both models along standard and design-based metrics, identifying key tradeoffs. Finally, we present a …
-
Imitation Learning with Superhuman Policy Gradient Optimization for Sequential Cancer Treatment Decisions
… simulator—combining a variational autoencoder (VAE) and gradient boosting (XGBoost) models—generates complete, temporally consistent patient trajec- tories, enabling safe and reproducible training. Unlike conventional behavior cloning, SPGO optimizes a subdominance loss that explicitly rewards …
-
Modelling non-linearity in 3D shapes: A comparative study of Gaussian process morphable models and variational autoencoders for 3D shape data
… (GPMM) and non-linear variational autoencoders (VAE). Their model performance is measured using generalisation, specificity and computational efficiency in training. The research showed that, given limited computational power, GPMMs managed to achieve improved relative generalisation performance …
Page 1 of 3