When machine learning meets complexity: why Bayesian deep learning is unavoidable

By now, all of you have probably followed deep learning research for quite a while. In 1998, LeCun et al. proposed the very simple MNIST data set of handwritten digits and showed with their LeNet-5 that we can achieve a high validation accuracy on it. The data sets subsequently proposed became more complex (e.g., ImageNet or Atari games), hence the models performing well on them became more sophisticated, i.e. complex, as well.
Read more