Summary: This paper has a very interesting claim: distributional shift in imitation learning settings is primarily caused by causal misidentification of the features by the learning algorithm. An interesting example is that of a self-driving car policy trained on a dataset of paired image-control datapoints collected by an expert human driving the car. If the images contain the turn signal on the dashboard then the supervised learner learns to have very good predictive power by indexing on that feature in the image. Clearly that does not generalize during test time. While this is a pathological example, such behavior is present in most settings where usually the state is blown-up by appending past states and actions.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Four years after its publication, The Building Blocks of Interpretability remains a seminal paper in the area of ML interoperability.

Books and courses where two main resources that I used to get to where I am right now. Among all the resources in this section, Kaggle stands out as one of the most amazing platforms on the internet for practicing and learning machine learning! Kaggle hosts lots of data science and deep learning competitions every year with participants taking part from all over the world. There are also loads of invaluable datasets on Kaggle that you can use to train your own models, all for free. The other amazing part of this platform is where people can share their code and solution to the competitions, all in a simple Jupyter notebook which makes experimenting with the code really easy.

I posted a question on the Computer Vision subreddit about the most important papers in Computer Vision and a lot of people recommended that I should familiarize myself with Machine Learning papers to be up to date on the latest of what is going on in Computer Vision. What are some of the most important papers in the field of Machine Learning for Computer Vision?