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 ...
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 ...
Recent advances in deep learning have led to the development of state-of-the-art models with remarkable accuracy; however, previous work has shown that these results incur a high environmental cost due to their significant ...
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 ...
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 ...
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 ...
Iterative stencil computations are important in scientific computing and in the mobile domain among others. Tiling is an important method to improve computational efficiency in GPUs, enhancing data reuse and cache memory ...
This thesis seeks to assess previous computational work done regarding reduplication in order to account for reduplication in a morphological analyzer for the Wamesa language. This paper includes a brief introduction to ...
In an age of ever expanding data sets, there is an increasing demand for high performance computing (HPC) in the scientific community. In order to maximize the performance of existing hardware, researchers have been looking ...
Teaching students the concepts and techniques for understanding theoretical computer science is an essential part of any computer science curriculum, yet thus far educators are struggling to help their students master this ...
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 ...
The growth in speed seen in modern processors over the past couple decades has created a need for programs that carefully manage the data fed to CPUs. Often, it is the case the processors are so fast, the memory used to ...
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 ...
People take for granted how much they use their vision when they shop, and the current online tools are not sufficiently supported or developed. We explore approaches to provide a more accurate and pleasant user experience. ...
In the technology industry and in academia, technologists frequently use the term "engagement" as one metric to measure a user's experience. Within the sub-field of Human-Computer Interaction (HCI), recently-published ...
In certain types of programs, re-ordering their execution can speed them up by optimizing memory accesses and parallelism. However, these re-orderings are constrained by the fact that updates depend on the results of earlier ...
Parallel and clustered implementations of scientific computing algorithms, while fast, can be very difficult for a human to parse. This is because the industry standard method of writing parallel code is to use complicated ...
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 ...
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 ...
My thesis focuses on strategies to analyze fairness in information spread in social networks. Building off the field of influence maximization, I examine how the spread of information in a social network advantages some ...