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By Emil Dudev, Aman Hussain, Omar Elbaghdadi, and Ivan Bardarov. Deep Q Networks (DQN) revolutionized the Reinforcement Learning world. It was the first algorithm able to learn a successful strategy in a complex environment immediately from high-dimensional image inputs. In this blog post, we investigate how some of the techniques...
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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...
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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...
