Institutional Scholarship

Browsing by Subject "Machine learning"

Browsing by Subject "Machine learning"

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  • Nicholas, Gareth (2020)
    This thesis outlines the methods used in machine learning to generate models which are effective on a variety of tasks. We begin with a quick overview of the field of machine learning, covering the topics necessary to ...
  • Xiong, Fangyu (2015)
    Active learning improves the efficiency of machine learning in situations where labels are acquired at a cost. In this paper, I explore how active learning techniques can be used in a lifelong machine learning setting, ...
  • St. Clair, Jack (2020)
    This paper will investigate different loss functions used for various natural language processing (NLP) machine learning tasks. These loss functions have proven their worth in the area of machine translation but they have ...
  • Griesman, Kendra (2020)
    Population genetics focuses on understanding the evolutionary history of specific populations to gain insight into evolutionary events leading to the variation observed in nature. Increasing our understanding of evolution ...
  • Feldman, Michael (2015)
    Machine learning algorithms called classifiers make discrete predictions about new data by training on old data. These predictions may be hiring or not hiring, good or bad credit, and so on. The training data may contain ...
  • Wang, Zhanpeng (2020)
    As the population genetic database such as 1000 Genomes Dataset (Consortium et al. 2015) grows in size every day, it becomes more and more challenging to understand the large flow of the genetic information. Recent works ...
  • Shukla, Rohan (2019)
    Authorship attribution is the process of identifying the author of a given work. This thesis surveys the history and foundations of authorship attribution, and then analyzes multiple machine learning methods that are used ...
  • Wang, Jiaping (2020)
    For the past years, researches in population genetics, a subfield of biology studying the variation of genes in a population with respect to space and time, rely heavily on simulated data. That is to say, analysis of the ...
  • Marx, Charles (2020)
    Methods for measuring fairness in machine learning often operate by quantifying the relationship between some protected feature (e.g., race or gender) and the predictions of a model. When we are interested in understanding ...
  • Thiel, Pablo (2020)
    Many problems in population genetics are well suited to supervised machine learning (ML) methods, which can leverage characteristics like high input dimensionality to result in considerable performance gains over traditional ...
  • Gerhard, Russell (2020)
    I review commonly used methods for approximating model parameters within the setting of population genetics and compare approximate Bayesian computation to a convolutional neural network. Results from population genetics ...
  • Roe, Conor Stuart (2020)
    In the SIGMORPHON 2019 shared task 1, multiple teams attempted for the first time to leverage transfer learning to build more accurate models of natural language morphology with small amounts of target language data, with ...

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