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In December 2020 I attended COLING’2020 (the 28th International Conference on Computational Linguistics) to present “Contextual BERT” at the TextGraphs-14 workshop and to learn about the latest advancements in natural language processing (NLP). This year the conference was not held […]
In December 2019 I had the pleasure to attend the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) in Vancouver, Canada, to present my work on document understanding. During the three main conference days there were a total of […]
In June 2017, Vaswani et al. published the paper “Attention Is All You Need” describing the “Transformer” architecture, which is a purely attention based sequence to sequence model. It can be applied to many tasks, such as language translation and text […]
Update: This guide applies to TF1. For TF2, use tf.keras.layers.BatchNormalization layer. 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. […]
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 […]