Using TensorFlow’s Batch Normalization Correctly

The TensorFlow library’s layers API contains a function for batch normalization: tf.layers.batch_normalization. It is supposedly as easy to use as all the other tf.layers functions, however, it has some pitfalls. This post explains how to use tf.layers.batch_normalization correctly. It does not delve into what batch normalization is, which can be looked up in the paper “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift” by Ioeffe and Szegedy (2015). Continue reading Using TensorFlow’s Batch Normalization Correctly

[Paper Recap] Multiple Hypotheses Prediction

The paper Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses was publish by Christian Rupprecht et al. in late 2016. The authors propose a training technique for machine learning models which makes them predict multiple distinct hypotheses. This is an advantage for many prediction tasks, in which uncertainty is part of the problem. In this article I am going to summarize the paper and name further thoughts. Continue reading [Paper Recap] Multiple Hypotheses Prediction