Generative probabilistic models of neuron morphology

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (page 40).Thanks to automation in ultrathin sectioning and confocal and electron microscopy, it is now possible to image large populations of neurons at single-cell resolution. This imaging capability promises to create a new field of neural circuit microanatomy. Three goals of such a field would be to trace multi-cell neural networks, to classify neurons into morphological cell types, and to compare patterns and statistics of connectivity in large networks to meaningful null models. However, those goals raise significant computational challenges. In particular, since neural morphology spans six orders of magnitude in length (roughly 1 nm-1 mm), a spatial hierarchy of representations is needed to capture micron-scale morphological features in nanometer resolution images. For this thesis, I have built and characterized a system that learns such a representation as a Multivariate Hidden Markov Model over skeletonized neurons. I have developed and implemented a maximum likelihood method for learning an HMM over a directed, unrooted tree structure of arbitrary degree. In addition, I have developed and implemented a set of object-oriented data structures to support this HMM, and to produce a directed tree given a division of the leaf nodes into inputs and outputs. Furthermore, I have developed a set of features on which to train the HMM based only on information in the skeletonized neuron, and I have tested this system on a dataset consisting of confocal microscope images of 14 fluorescence-labeled mouse retinal ganglion cells. Additionally, I have developed a system to simulate neurons of varying difficulty for the HMM, and analyzed its performance on those neurons. Finally, I have explored whether the HMMs this system learns could successfully detect errors in simulated and, eventually, neural datasets.by Stephen Rothrock Serene.M. Eng

Generative probabilistic models for image retrieval

Searching for information is a recurring problem that almost everyone has faced at some point. Being in a library looking for a book, searching through newspapers and magazines for an old article or searching through emails for an old conversation with a colleague are some examples of the searching activity. These are some of the many situations where someone; the “user”; has some vague idea of the information he is looking for; an “information need”; and is searching through a large number of documents, emails or articles; “information items”; to find the most “relevant” item for his purpose. In this thesis we study the problem of retrieving images from large image archives. We consider two different approaches for image retrieval. The first approach is content based image retrieval where the user is searching images using a query image. The second approach is semantic retrieval where the users expresses his query using keywords. We proposed a unified framework to treat both approaches using generative probabilistic models in order to rank and classify images with respect to user queries. The methodology presented in this Thesis is evaluated on a real image collection and compared against state of the art methods

An investigation of techniques that aim to improve the quality of labels provided by the crowd

The 2013 MediaEval Crowdsourcing task looked at the problem of working with noisy crowdsourced annotations of image data. The aim of the task was to investigate possible techniques for estimating the true labels of an image by using the set of noisy crowdsourced labels, and possibly any content and metadata from the image itself. For the runs in this paper, we’ve applied a shotgun approach and tried a number of existing techniques, which include generative probabilistic models and further crowdsourcing

Generative probabilistic models for object segmentation

One of the long-standing open problems in machine vision has been the task of ‘object segmentation’, in which an image is partitioned into two sets of pixels: those that belong to the object of interest, and those that do not. A closely related task is that of ‘parts-based object segmentation’, where additionally each of the object’s pixels are labelled as belonging to one of several predetermined parts. There is broad agreement that segmentation is coupled to the task of object recognition. Knowledge of the object’s class can lead to more accurate segmentations, and in turn accurate segmentations can be used to obtain higher recognition rates. In this thesis we focus on one side of this relationship: given the object’s class and its bounding box, how accurately can we segment it? Segmentation is challenging primarily due to the huge amount of variability one sees in images of natural scenes. A large number of factors combine in complex ways to generate the pixel intensities that make up any given image. In this work we approach the problem by developing generative probabilistic models of the objects in question. Not only does this allow us to express notions of variability and uncertainty in a principled way, but also to separate the problems of model design and inference. The thesis makes the following contributions: First, we demonstrate an explicit probabilistic model of images of objects based on a latent Gaussian model of shape. This can be learned from images in an unsupervised fashion. Through experiments on a variety of datasets we demonstrate the advantages of explicitly modelling shape variability. We then focus on the task of constructing more accurate models of shape. We present a type of layered probabilistic model that we call a Shape Boltzmann Machine (SBM) for the task of modelling foreground/background (binary) and parts-based (categorical) shapes. We demonstrate that it constitutes the state-of-the-art and characterises a ‘strong’ model of shape, in that samples from the model look realistic and that it generalises to generate samples that differ from training examples. Finally, we demonstrate how the SBM can be used in conjunction with an appearance model to form a fully generative model of images of objects. We show how parts-based object segmentations can be obtained simply by performing probabilistic inference in this joint model. We apply the model to several challenging datasets and find that its performance is comparable to the state-of-the-art

Essays in finance: Generative probabilistic models, firm efficiency, and investor relations

This dissertation consists of four independently written essays dealing with inference and prediction of financial data sets. The first part of this dissertation focuses on the inference element and contains two chapters that explore the question of how financial markets priced companies’ stocks during the market collapse caused by the COVID-19 pandemic in the beginning of 2020. As the COVID-19 pandemic and the subsequent economic lockdown represented one of the most impacting exogenous shocks to financial markets in recent history, it led to a huge increase in uncertainty about a firm’s future cash flows. This environment thus allowed us to examine the drivers and characteristics that may make firms more resilient to crises and help to reduce investor uncertainty. The last two essays of this dissertation move away from the inference element and deal with the prediction of financial time series data using unsupervised machine learning methods. In the finance literature so far, machine learning models are mainly used for discriminative tasks, such as point forecasts or classifications. However, in this dissertation, we show how the finance literature can be extended by using generative probabilistic models, which aim to learn the underlying distribution of the data and are able to generate realistic artificial samples. Since time series in the real world are highly stochastic, probabilistic sampling has the advantage of providing a complete distribution of possible scenarios instead of a single prediction

EntiTables: Smart Assistance for Entity-Focused Tables

Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specific tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.Comment: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17), 201