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Our research is strongly interdisciplinary. It concerns using neural networks and other computational models applied to problems in cognitive science and artificial intelligence, engineering and biology. I have had success using them for such disparate tasks as modeling how children acquire words, studying how lobsters chew, and nonlinear data compression. Most recently I have worked on face and object recognition, visual salience and visual attention, and modeling early visual cortex.
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MEDICAL IMAGE LEARNING WITH LIMITED AND NOISY DATA (MILLAND 2022) (2022): 229-238
Proceedings of the 2019 International Conference of The Computational Social Science Society of the AmericasSpringer Proceedings in Complexitypp.1-14, (2021)
arxiv(2020)
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