On 01/02/2014 10:46 AM, Gael Varoquaux wrote: >> In my opinion, Adagrad is still on the lower side in terms of number of >> citations (currently 107 according to Google) for inclusion into >> scikit-learn. >> So unless there is strong evidence that it outperforms other solvers (e.g., >> in >> computer vision or NLP papers that use Adagrad), I'm personally -1 for its >> inclusion into scikit-learn. > Agreed. Also, there is currently a lot of progress on improving > stochastic gradient based solvers. In particular at the latest NIPS and > ICML. Ideally, I think that it is best to wait a little bit for the dust > to settled down and then implement what comes out as the best option (I > think that my favorite is SAG, but let's wait and see). +1 (If I haven't said so already - not necessarily on SAG though ;-)
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