Seven Myths in Machine Learning Research

18 Feb 2019  ·  Oscar Chang, Hod Lipson ·

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 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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