Hi Marcus,
> Sounds great especially maxout is interesting, let us know if you need any > help > with the implementation. > Sure. I have nearly finished on PReLU implementation. After that I will get to maxout units. > Sorry, I didn't realize that one of the papers isn't accessible for free, > here > are two more papers that are interesting: > Not an issue. > "An improved radial basis function neural network for object image > retrieval” by > G. A. Montazer et al. - https://pdfs.semanticscholar.org/2f62/ > ff139763c697674e447825bc214653b97192.pdf > > "A Comparison of RBF Neural Network Training Algorithms for Inertial > Sensor Based Terrain Classification” by > T. Kurban et al. - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312446/ > Thanks for the updated links. > That highly depends on the models you like to implement e.g. implementing > Bidirectional Recurrent networks is straightforward since there is already > an > RNN class that can be used to construct the model or if you like to > implement > RBMs the step to DBN's is also fairly straightforward since you can stack > RBM's > to build you DBN. On the other side the implementation of the main idea > might be > straightforward but for each model, there are a bunch of interesting > features > that can be explored like different training methods so that you could > easily > work entirely on a single model. > > I would recommend that when you write your proposal; define some main > goals that > you think are realistic to achieve and probably add some other features > that you > like to work on if there is time left. > Thanks for the clarification. I will submit PR when I have finished implementing PReLU completely. Thanks, Prasanna
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