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Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?

• Fede_V 11y ago
  Sure. 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).
  • JD557 11y ago
    Also, it might be worth adding that random forests are based on decision trees, which can be interpreted by humans. 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.
    • Fede_V 11y ago
      Random forests are very difficult to interpret especially once you start having lots of trees.
    • rm999 11y ago
      >While it's hard to interpret a random forest, it's still much more interpretable than a neural network 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.
  • UmDieWelt 11y ago
    New frontier? They've been popular for over 20 years.
    • robotresearcher 11y ago
      That's AI. Every generation invents everything again. Probably works this way in other fields too. (Yes this is hyperbole, but any old timers out there will know the feeling)
      • rdtsc 11y ago
        > That's AI. Every generation invents everything again. 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.
    • Fede_V 11y ago
      They really came to the forefront with alex.net - GPUs plus lots of labeled examples made them suddenly very practical.
      • simonhughes22 11y ago
        Yes and No. Layer to layer training of deep belief nets were doing very well before alex.net came along, although alex.net was seminal for images. People often miss the big 'advances' in RNN's, although those go back to the 90's and earlier. A lot of this (but not all) is also due to more data, better and faster hardware (including GPU's). Although there have been some important algorithmic enhancements too.
    • mtrimpe 11y ago
      Regular neural networks were popular until they stopped delivering best-in-class results for a lot of problems. 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.
    • eva1984 11y ago
  • obastani 11y ago
    I think there are a few additional important details. 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.
  • YeGoblynQueenne 11y ago
    >> One of the main theoretical results in the paper is that they propose a differentiable .. technique to optimize the parameters of the trees. Yay! Now Decision Trees too can get stuck in local optima! :D (totally tongue in cheek)
    • argonaut 11y ago
      Even though this is tongue-in-cheek, it makes no sense. Decision trees are traditionally trained in a greedy, non-globally-optimal fashion.
      • YeGoblynQueenne 11y ago
        What I mean is that decision trees are not seen as optimising a function so it doesn't make much sense to think of them as finding optima, or getting stuck in sub-optima (if I may). 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.
• parvatzar 11y ago
  I second that question!
• gamegoblin 11y ago
  Super high level of deep nets, skip if you already know: Deep nets are good at taking a vector of size N and transforming it to a vector of size M, where M is perhaps a more general, abstract, or "useful" representation. e.g. Your N-vector might be a length 786 vector of floating point values representing black-intensity in a 28x28 grayscale image. This is the case in the MNIST dataset (lots of 28x28 images of the digits 0-9), a classic dataset in machine learning. After a layer or two of a deep net, this N-vector might be transformed into an M-vector where each component represents some particular edge, curve, or blip within the source image. So you've gone from the representation of "pixel 0 is gray, pixel 1 is dark gray, pixel 2 is white..." to a representation of "There is a vertical edge on the central lefthand side of the image, there is an upwards facing curve in the central top part of the image....". It's clear that the latter representation is more compact and useful for the purpose of digit recognition. It's also worth noting this representation is specific to the problem at hand. The edges and curves you have learned would probably be unable to accurately reproduce say, letters of the alphabet, as they are specialized to reproducing digits. The net has learned a more compact representation by using statistics to figure out that most of the information is redundant. There are only 10 possible outputs, but the input space is 256 grayscale values ^ 768 pixels. In a traditional deep net, your output layer for this particular problem (digit recognition) might be a vector of length 10, where element of the vector is the probability of that digit being the one shown. So a result of <0.1, 0.1, 0.998, 0.0 ... 0.0> would indicate that the net thought the digit was a 2. ================ Super high level of decision trees and forests, skip if you already know: A decision tree is somewhat self-explanatory -- it's kind of like a flow chart for making judgments. Here is an example, classifying cool vs. uncool based on 3 attributes. My dataset: | bow_tie | socks | sandals | cool ------|---------|-------|---------|------ Alice | true | true | true | true Bob | false | true | true | false Carol | false | true | false | true Doug | true | false | true | true Ella | false | false | false | false A possible decision tree: if bow_tie == true return true else if socks == true if sandals == true return false else return true else if sandals == true return true else return false It's also worth noting that decisions trees can be equal with different representation. The following will always return the same value as the above for the elements of the dataset: if socks == true if sandals == true if bow_tie == true return true else return false else return true else if sandals == true return true else if bow_tie == true return true else return false The difference is how you pick the divisions. The first one uses a more entropy-reducing strategy -- we notice that the bow_tie division is a simple, hard rule. Bowties are cool. The second is more of a random decision decision tree, so it's less "efficient" in that it must make potentially more judgments. Why would we ever want to be less efficient? It turns out if you train several (hundreds, thousands, etc) decision trees on subsets of the data, and then average their results together, they are alarmingly good classifiers. Extremely simple to code, train, and use. This is called a decision forest. A decision tree on its own is often weak, but decision forests are a powerful tool. ================ High level of why this work is interesting: The traditional means of training a deep neural net is with gradient descent. The most common form of this is some method of "backpropagation". You run a training example through your network, calculate the error between the result and expected result, and then propagate this error gradient back through the network to tune the transformations to produce closer to what you want. This method often requires the functions you use within the deep network to be differentiable. As mentioned above, there are several training strategies for decision trees, but the most common is some form of "mostly random". To extend my example from the first section, one could use the deep neural net to transform the 768 grayscale pixel values into perhaps 30 higher level edge/curve features. Then one could use this length 30 vector as the input to train a decision forest. This might end up getting better results than either strategy by itself. You use the neural net to do the abstracting and the decision forest to make the final decision. This uses both of their advantages in tandem -- deep neural nets are great at generating more abstract and general features, and decision forests are quite good at producing accurate classifications given high-quality, lower-dimensional input data. This idea of multi-tiered systems isn't particularly new. What this paper does, though, is introduce a differentiable decision tree. This means that they can train their decision trees with gradient descent, the same way they train the neural network. This means that, rather than training the two tiers of their system individually, they can train them together, producing even better results.
• freedryk 11y ago
  To elaborate on Fede_V's comment, Deep Neural Networks seem to work well on classification problems because they can automatically build abstract features from a dataset and combine them in different ways. Like, in image data they will automatically identify common shapes and patterns of light and dark, and combine these simple patterns together to identify faces or whatever (like by saying a face is a circle with two dots for eyes and a line for a mouth). Random Forests, on the other hand, are really good at classifying things if you give them meaningful dataset features to learn on, but aren't as good at building these features in the first place. By combining them together, they get a system with the classification abilities of random forests and the automated feature discovery of deep neural nets, and it seems to work a bit better than either.
  • simonhughes22 11y ago
    They currently really excel at unstructured data - images and text and speech. If you have structured data though (columns, regular features) it really depends. In Kaggle, RF's and GBT's seem to mostly dominate structured problems, while neural nets dominate unstructured datasets (as they can do feature extraction), according to talk I attended.