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Check out this latest paper from CSE researchers showing #ML time-series prediction methods can forecast synthetic & experimental cardiac action potentials better than other methods!🫀https://t.co/Abc9ndkkRe
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Our paper is finally published and is available online. In this paper, we showed how various architectures of Echo State Networks (ESNs) can accurately predict nonlinear time series in real-world applications, while they’re about 100X faster than other RNN
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🎉Congrats to CSE Associate Prof. Elizabeth Cherry, Ph.D. student Shahrokh Shahi, & Postdoc Christopher D. Marcotte! 🎉 Their paper on predicting cardiac voltage data using RNNs and ESNs is now published in @FrontPhysiol and can be read here: https://t.