Max norm regularization consists in clipping each neuron's weight vector after each training step to ensure that its norm never exceeds some threshold.
I am looking for the original paper that described this technique. Using scholar.google.com, I found a couple of papers that people seem point to in papers about max norm, but they don't seem to correspond to the technique described above. They're about matrix factorization (and way out of my league):
- Maximum-Margin Matrix Factorization, 2004, N. Srebro et al.
- Practical Large-Scale Optimization for Max-Norm Regularization, 2010, J. Lee et al.