How Neural Networks (NN) Can (Hopefully) Learn Faster by Taking into Account Known Constraints
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Neural networks are a very successful machine learning technique. At present, deep (multi-layer) neural networks are the most successful among the known machine learning techniques. However, they still have some limitations, One of their main limitations is that their learning process still too slow. The major reason why learning in neural networks is slow is that neural networks are currently unable to take prior knowledge into account. As a result, they simply ignore this knowledge and simulate learning “from scratch”. In this paper, we show how neural networks can take prior knowledge into account and thus, hopefully, learn faster.
This work was supported in part by NSF grants HRD-0734825, HRD-1242122, and DUE-0926721, and by an award from Prudential Foundation.