I am looking for papers or texts that compare and discuss (either empirically or theoretically):

• Boosting and Decision trees algorithms such as Random Forests or AdaBoost, and GentleBoost applied to decision trees.

with

• Deep learning methods such as Restricted Boltzmann Machines, Hierarchical Temporal Memory, Convolutional Neural Networks, etc.

More specifically, does anybody know of a text that discusses or compares these two blocks of ML methods in terms of speed, accuracy or convergence? Also, I am looking for texts that explain or summarize the differences (e.g. pros and cons) between the models or methods in the second block.

Any pointers or answers addressing such comparisons directly would be greatly appreciated.