Waves.L3.Multimaximizer.v1.0-H2O 💾





 
 
 
 
 
 
 

Waves.L3.Multimaximizer.v1.0-H2O

the multimaximizer is a simple neural network, that assigns a high label to nodes based on the maximized values of an output node. this task is very easy and everyone can do this task in his/her spare time.

implements maximization using the gradient descent method.

field type description
dzd dzd difference (l4,l1) > 0?
dwc dzd difference (l4,l1) < 0?
dwd dzd difference (l4,l2) > 0?
dwd dzd difference (l4,l2) < 0?
dww dzd difference (l4,l3) > 0?
dww dzd difference (l4,l3) < 0?
el float maximum learning rate.
epsilon float step size of the minimizer
name string name of the model

this model is designed to predict the maximal transcriptional activity of the human genes that have been characterized in the homo sapiens species, but not of the orthologous genes in the other species. e.g., progress

// this multilinear maximum likelihood (ml) code is derived from the auto-tuning // l3 code developed by philip langdon (which uses p-norms rather than generic // c-subspaces as described in [1]) and has been adapted for h2o by gustavo // pratesi. it is not officially supported by h2o, but can be used freely. the // differences between the langdon code and ours are:

  • langdon uses a c-subspace penalty and linear penalization for regularization, whereas we use a weighted c-subspace penalty for regularization (as described in section i ).
  • langdon’s code uses c-subspaces and linear penalization , whereas ours uses weighted c-subspaces .
  • the langdon code uses a extra regularization term (encoding the training samples and the learning rates for each datapoint) which we do not (although it is worth noting that the extra regularization term can be written as a c-subspace regularization , so our code could in principle use this extra regularization term as well).

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