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lit_survey.md

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Literature Survey

This file will include summaries of papers read or identified as worth reading. It will be a companion to an overleaf document of similar purpose.

If there is no comment beneath a paper, assume it hasn't been read yet.

Motivating Problems

  • Motivation for and Evaluation of the First Tensor Processing Unit (TPU).
    • Paper by Google about their TPU. Hardware description is unlikely to be relevant to our work, but the paper discusses the workload properties of DNN workflows and their importance.

Letter Transposition Problem

  • Andrews, S (1996) Lexical retrieval and selection processes: Effects of transposed-letter confusability. Journal of Memory and Language, 35(6), 775-800.
    • Substantial discussion about different models of how people identify words, and their various failings.
    • Experiments look at the effect of letter transposition on word identification; it appears that people are pretty good at recognizing actual words regardless of being close to other higher frequency words, but that it takes people longer to catch on that a nonword formed by switching two letters in a real, frequent word is wrong.
  • Healy, A. F. (1976). Detection errors on the word The: Evidence for reading units larger than letters. Journal of Experimental Psychology: Human Perception & Performance, 2, 235-242.
  • McCusker, L. X., Gough, P. B., Bias, R. G. (1981) Word recognition inside out and outside in. Journal of Experimental Psychology: Human Perception and Performance, 7(3), 538-551.
  • Shillcock, R., Ellison, T.M. & Monaghan, P. (2000). Eye-fixation behaviour, lexical storage and visual word recognition in a split processing model.Psychological Review 107, 824-851.
  • Van-Orden, G. C. (1987) A ROWS is a ROSE: Spelling, sound, and reading. Memory and Cognition, 15(3), 181-198.
  • Miller, G. A., Heise, G. A., & Lichten, W. (1951). The intelligibility of speech as a function of the context of the test materials. Journal of Experimental Psychology, 41, 329-335.

Machine Learning Algorithms

Topic Models

Word Embeddings

Language Models