It is well known that deep networks tend to outperform shallow networks and other classical machine learning techniques such as boosting on learning tasks involving images. I believe this is because extracting useful high-level features from low-level pixel intensities requires deep models.
I'm curious to know if there are any non-image data sets (e.g., tabular datasets that are typically found in UCI repository) where deep networks are required for good performance and where shallow networks, boosting tend to perform poorly.