Christopher M. Ormerod
California Institute of Technology
50 Papers
100 Citations
Christopher M. Ormerod is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Lax pair & Computer science. The author has an hindex of 12, co-authored 45 publications. Previous affiliations of Christopher M. Ormerod include La Trobe University & University of Melbourne.
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Papers
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Language models and Automated Essay Scoring
TL;DR: The current state-of-the-art natural language processing (NLP) neural network architectures are used in this work to achieve above human-level accuracy on the publicly available Kaggle AES dataset.
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Discrete Painlevé equations and their Lax pairs as reductions of integrable lattice equations
TL;DR: In this paper, a method to obtain Lax pairs for periodic reductions of a rather general class of integrable non-autonomous lattice equations was described. But the method was applied to obtain reductions of the nonautonomous discrete Korteweg-de Vries equation and the non autonomous discrete Schwarzian Korte-de-Vries equation, which yield a discrete analogue of the fourth Painleve equation.
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Commutation Relations and Discrete Garnier Systems
TL;DR: In this paper, the authors present four classes of nonlinear systems which may be considered discrete analogues of the Garnier system, which arise as discrete isomonodromic deformations of linear difference equations in which the associated Lax matrices are presented in a factored form.
Twisted reductions of integrable lattice equations, and their Lax representations
TL;DR: In this article, the authors generalize the periodicity condition by adding a symmetry transformation and apply this idea to autonomous and non-autonomous lattice equations, obtaining new reductions of the discrete potential Korteweg-de Vries (KdV) equation, discrete modified KdV equation and the discrete Schwarzian kdVries equation.
Automated Short Answer Scoring Using an Ensemble of Neural Networks and Latent Semantic Analysis Classifiers
Christopher M. Ormerod,Susan M. Lottridge,Amy E. Harris,Milan Patel,Paul van Wamelen,Balaji Kodeswaran,Sharon Woolf,Mackenzie Young +7 more
TL;DR: An ensemble of deep neural networks and a Latent Semantic Analysis-based model is introduced to score short constructed responses for a large suite of questions from a national assessment program and achieves above-human-level performance on a large set of items.
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