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arXiv · 1902.06789

Seven Myths in Machine Learning Research

Oscar Chang·Hod Lipson
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Abstract

We present seven myths commonly believed to be true in machine learning research, circa Feb 2019. This is an archival copy of the blog post at https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/ Myth 1: TensorFlow is a Tensor manipulation library Myth 2: Image datasets are representative of real images found in the wild Myth 3: Machine Learning researchers do not use the test set for validation Myth 4: Every datapoint is used in training a neural network Myth 5: We need (batch) normalization to train very deep residual networks Myth 6: Attention $>$ Convolution Myth 7: Saliency maps are robust ways to interpret neural networks

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Oscar Chang, Hod Lipson. 2019-02-18. Seven Myths in Machine Learning Research. https://arxiv.org/abs/1902.06789

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