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16 May 2019

A joint post with Ofri Mann   We went to ICLR to present our work on debugging ML models using uncertainty and attention. Between cocktail parties and jazz shows in the wonderful New Orleans (can we do all conferences in NOLA please?) we also saw a lot of interesting talks and posters. Below are our main takeaways from the conference. Main themes A good summary of the themes was in Ian Goodfellow’s talk, in which he said that until around 2013 the ML community was focused on making ML work. Now that it’s working on many different applications given enough data, the focus has shifted towards adding more capabilities to our models: we want them to comply to some fairness, accountability and transparency constraints, to be robust, use labels efficiently, adapt to different domains and so on. A slide on ML topics, from Ian Goodfellow’s talk   We noticed a […]

25 April 2018

Back in 2012, when neural networks regained popularity, people were excited about the possibility of training models without having to worry about feature engineering. Indeed, most of the earliest breakthroughs were in computer vision, in which raw pixels were used as input for networks. Soon enough it turned out that if you wanted to use textual data, clickstream data, or pretty much any data with categorical features, at some point you’d have to ask yourself – how do I represent my categorical features as vectors that my network can work with? The most popular approach is embedding layers – you add an extra layer to your network, which assigns a vector to each value of the categorical feature. During training the network learns the weights for the different layers, including those embeddings. In this post I will show examples of when this approach will fail, introduce category2vec, an alternative method […]

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