# Dual Process Theory and Machine Learning

Can the influential ideas from Kahnemann and others' research about humans having two modes of thinking, one fast, one slow, be applied to current machine learning systems

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Learning to learn

Can the influential ideas from Kahnemann and others' research about humans having two modes of thinking, one fast, one slow, be applied to current machine learning systems

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Collection of some recent work on uncertainty estimation for deep learning models using Bayesian and non-Bayesian methods

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An introduction and comparison of two popular techniques for estimating gradients in machine learning models

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Highlighting ideas at the intersection of cognitive science and machine learning by summarizing the work of Lake et al (2016) and its related paper commentaries

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How can we define what an interpretable model is and why is it even an important question to ask

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Exploring the relationship between variational inference and expectation maximization algorithm

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