Timo Denk's Blog

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Tag: Deep learning

My Favorite Papers at COLING’2020

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 […]

December 13, 2020December 13, 2020by Timo Denk

My Favorite Papers at NeurIPS 2019

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 […]

December 18, 2019April 18, 2020by Timo Denk

Linear Relationships in the Transformer’s Positional Encoding

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 […]

January 22, 2019January 22, 2019by Timo Denk

Using TensorFlow’s Batch Normalization Correctly

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. […]

October 14, 2018May 28, 2020by Timo Denk

[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 […]

October 5, 2017July 3, 2020by Timo Denk

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This blog is run by me, Timo Denk. I am an Applied Scientist with Zalando living in Berlin, Germany. Please visit my portfolio website for more detailed information.

Editor’s choice

If you just came here and do not know where to go, I recommend looking into these posts:

  • Digit Span Test (most viewed)
  • Cubic Spline Interpolation (math-heavy)
  • Commute Time Tracking (nice read)
  • Linear Relationships in the Transformer’s Positional Encoding (machine learning)
  • Corsairs3D (pirate game – arrr!)

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