Archive of posts with category 'machine-learning'
One of the most exciting applications of advancements in artificial intelligence has been the modelling of protein structures and protein folding. Last year DeepMind announced their AlphaFold project, which claims...
And as quickly as that, we’re at task 6 of our quest. Causal imitation learning is perhaps the most fanciful-sounding, but at it’s core it remains as simple a goal...
We’ve now come to one of the most vital aspects of this theory - how can we learn causal models? Learning models is often an exceptionally computationally intensive process, so...
At this point we’ve developed a good sense of the technical theory of causal reinforcement learning. This next section brings together many important ideas and generalises notions of data transfer...
In the previous blog post we discussed and motivated the need for a causal approach to reinforcement learning. We argued that reinforcement learning naturally falls on the interventional rung of...
As part of any honours degree at the University of Cape Town, one is obliged to write a thesis droning on about some topic. Luckily for me, applied mathematics can...
Last time we discussed how we can learn causal structure from data and thought about how this relates to machine learning. Specifically, we noticed that having more data in a...
Last time we briefly discussed the theory needed to start thinking about how we can learn, in the statistical sense, causal information from ‘dumb’ data. Some key points were that...
In the last episode we developed the first tools we need to develop the theory needed to formalise interventions and counterfactual reasoning. In this article we’ll discuss how we can...
Perhaps we could use uncertainty estimation to detect where the model may be wrong and then correct for these potential errors without having to collect much more data. This uncertainty...
Where we are Up to this point we have discussed methods primarily relying on the learning of value functions, usually approximating these with some neural network. That is, our focus...
The World Models (Ha et al., 2018) paper presented at NIPS in 2018 exploits the idea of having an agent train entirely within its latent representation of the world it...
Model-free reinforcement learning algorithms are a class of algorithms which do not use the transition probability information to train and make decisions. In a sense, they are a class of...
