It's not so much that they stack a random forest on top of a neural network, as that they are trying to train neural networks that have tree-like properties. Deep neural networks can be expensive to evaluate because every layer is needed to compute the resulting label. On the other hand, in a decision tree you only need to evaluate one of a nodes children (i.e., evaluation is linear in the depth of the tree).
They combine these ideas by allowing some nodes in the network to branch (so for a given image, you choose one of two neural networks to evaluate). These nodes don't have to occur at the top level, but can occur in the middle of the network as well.
The difficulty is how you train these nodes; that's where the differential technique for optimizing trees come in. Because you have a unified training algorithm, you can mix and match tree-like nodes and neural network like nodes and train the whole thing.
All in all, the hope is that getting a label for a given node is much faster, so you can run these on smaller devices such as phones without sacrificing accuracy.
While it's hard to interpret a random forest, it's still much more interpretable than a neural network. Maybe this model is also easier to interpret by humans.
Depends on the domain. The first layer or two of a neural network can be fairly interpretable on image or spectrogram (sound) data, for example.
I clearly remember in my machine learning class, one professor mentions neural networks and says it is slow and impossible to tame when the layers goes up thus loses its popularity. That is just 3-4 years ago.
Deep learning has really only recently become successful
hinton coined the term "deep learning" around 2006/2007 (more around deep belief nets/RBMs, but still, same thing), if that's considered "recently."
constrastive divergence and convolutional neural networks.
CD was also ~10 years ago. CNNs were reading your checks and postal zipcodes in the mid 90s.
successfully train the network.
In the early 90s, RNNs were driving cars on highways using only webcams under basically VFR. No giant sensors, no LIDAR, no GPS, no mesh networks, just camera input.
Before that there was little evidence that any form of neural network was massively better than other forms of machine learning. Now it has become clear that isn't the case.
Recent hardware made it possible to train more layers and they're now getting cutting-edge results in many areas again so they're now getting more attention again.
(Yes this is hyperbole, but any old timers out there will know the feeling)
Also "Past decades was AI winter. But no worries, now we'll start seeing human intelligence in a about next year" probably heard that since late 90's.
Yay! Now Decision Trees too can get stuck in local optima! :D
(totally tongue in cheek)
That's the traditional view. I guess it's always possible to see decision trees as optimising a binary function, but that's probably to be filed under "original research". Or maybe not- I'm not sure.
In any case, I've never heard of anyone worried that a tree learner would get stuck anywhere. The concern is usually with overfitting.
Random forests are a very powerful machine learning method that has proven to give excellent performance with very little tuning. Neural networks with many layers (aka deep learning) is the new frontier of machine learning - they are very powerful but require extensive tuning (the architecture of the network) and lots of examples before they become practical.
In this paper, the authors combine the two methods - they basically stack a random forest on top of a neural network, and obtain some cutting edge results. One of the main theoretical results in the paper is that they propose a differentiable (which means you can use gradient-based optimization techniques) technique to optimize the parameters of the trees (which are the building blocks of the random forest).