network machine learning

Journal Club: Chen et al, 2018: Neural Ordinary Differential Equations

In the 2018 NeurIPS conference, 4,845 papers were submitted. The paper I’m reviewing here by Chen et al, 2018, titled Neural Ordinary Differential Equations, won best paper award. The paper discusses using continuous Ordinary Differential Equations (ODE) for Neural Networks (NN) as opposed to the sorts of discrete layers used in the standard Recurrent Neural Networks (RNN).

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Journal Club: Harper et al 2017

It may be self-serving, but I thought it also apt for the first article I review for Journal Club be my own. My paper, titled Multiple substitutions lead to increased loop flexibility and expanded specificity in Acinetobacter baumannii carbapenemase OXA-239, is the culmination of my undergraduate research. I was looking at a clinical variant of the beta-lactamase enzyme OXA-23 which has 3 amino acid substitutions.

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