Deep Neural Decision Forests [pdf]
research.microsoft.com
Deep Neural Decision Forests [pdf]
1–10 of 38 posts
Re: Deep Neural Decision Forests [pdf]
#2Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?
Re: Deep Neural Decision Forests [pdf]
#3Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?
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).
Re: Deep Neural Decision Forests [pdf]
#4Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?
I second that question!
Re: Deep Neural Decision Forests [pdf]
#5Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?
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 -…
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.
Re: Deep Neural Decision Forests [pdf]
#6Earlier quoted context omitted.
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 -…
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.
Random forests are very difficult to interpret especially once you start having lots of trees.
Re: Deep Neural Decision Forests [pdf]
#7Could someone explain in layman words what is the qualitative contribution of the paper and why it is important?
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 -…
New frontier? They've been popular for over 20 years.
Re: Deep Neural Decision Forests [pdf]
#8[deleted]
Re: Deep Neural Decision Forests [pdf]
#9Earlier quoted context omitted.
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 -…
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.
>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.
Re: Deep Neural Decision Forests [pdf]
#10Earlier quoted context omitted.
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 -…
New frontier? They've been popular for over 20 years.
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)