Global ETD Search

1	VConstruct: a computationally efficient method for reconstructing satellite derived Chlorophyll-a data Ehrler, Matthew 31 August 2021 (has links) The annual phytoplankton bloom is an important marine event. Its annual variability can be easily recognized by ocean-color satellite sensors through the increase in surface Chlorophyll-a concentration, a key indicator to quantitatively characterize all phytoplankton groups. However, a common problem is that the satellites used to gather the data are obstructed by clouds and other artifacts. This means that time series data from satellites can suffer from spatial data loss. There are a number of algorithms that are able to reconstruct the missing parts of these images to varying degrees of accuracy, with Data INterpolating Empirical Orthogonal Functions (DINEOF) being the most popular. However, DINEOF has a high computational cost, taking both significant time and memory to generate reconstructions. We propose a machine learning approach to reconstruction of Chlorophyll-a data using a Variational Autoencoder (VAE). Our method is 3-5x times faster (50-200x if the method has already been run once in the area). Our method uses less memory and increasing the size of the data being reconstructed causes computational cost to grow at a significantly better rate than DINEOF. We show that our method's accuracy is within a margin of error but slightly less accurate than DINEOF, as found by our own experiments and similar experiments from other studies. Lastly, we discuss other potential benefits of our method that could be investigated in future work, including generating data under certain conditions or anomaly detection. / Graduate VAE deep learning chl-a reconstruction computational efficency cloud removal generative modelling
2	Generative Modelling and Probabilistic Inference of Growth Patterns of Individual Microbes Nagarajan, Shashi January 2022 (has links) The fundamental question of how cells maintain their characteristic size remains open. Cell size measurements made through microscopic time-lapse imaging of microfluidic single cell cultivations have posed serious challenges to classical cell growth models and are supporting the development of newer, nuanced models that explain empirical findings better. Yet current models are limited, either to specific types of cells and/or to cell growth under specific microenvironmental conditions. Together with the fact that tools for robust analysis of said time-lapse images are not widely available as yet, the above-mentioned point presents an opportunity to progress the cell growth and size homeostasis discourse through generative, probabilistic modeling and analysis of the utility of different statistical estimation and inference techniques in recovering the parameters of the same. In this thesis, I present a novel Model Framework for simulating microfluidic single-cell cultivations with 36 different simulation modalities, each integrating dominant cell growth theories and generative modelling techniques. I also present a comparative analysis of how different Frequentist and Bayesian probabilistic inference techniques such as Nuisance Variable Elimination and Variational Inference work in the context of a case study of the estimation of a single model describing a microfluidic cell cultivation. Generative modelling probabilistic inference Nuisance Variable Elimination Variational Inference microfluidic single cell cultivations Computer Systems Datorsystem Probability Theory and Statistics Sannolikhetsteori och statistik Bioinformatics (Computational Biology) Bioinformatik (beräkningsbiologi)
3	Reparametrization in deep learning Dinh, Laurent 02 1900 (has links) No description available. Neural networks Deep neural networks Machine learning Deep learning Unsupervised learning Probabilistic modelling Probabilistic models Generative modelling Generative models Generator networks Variational inference Generalization Reparametrization trick Réseaux de neurones Réseaux neuronaux Réseaux de neurones profonds Réseaux neuronaux profonds Apprentissage automatique Apprentissage profond Apprentissage non-supervisé Modélisation probabiliste Modélisation générative Modèles probabilistes Modèles génératifs Réseaux générateurs Inférence variationnelle Généralisation Astuce de la reparamétrisation

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