Recent posts

Self-Explaining Neural Networks: A Review

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For many applications, understanding why a predictive model makes a certain prediction can be of crucial importance. In the paper “Towards Robust Interpretability with Self-Explaining Neural Networks”, David Alvarez-Melis and Tommi Jaakkola propose a neural network model that takes interpretability of predictions into account by design. In this post, we...

Variational Bayesian Inference: A Fast Bayesian Take on Big Data.

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Compared to the frequentist paradigm, Bayesian inference allows more readily for dealing with and interpreting uncertainty, and for easier incorporation of prior beliefs. A big problem for traditional Bayesian inference methods, however, is that they are computationally expensive. In many cases, computation takes too much time to be used reasonably...

Hello World!

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Hi there! This is my first blog post ever!