Al
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7d727fc8f0
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[optimization] Using adapted learning rate in stochastic gradient descent (if lambda > 0)
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2016-01-17 20:59:47 -05:00 |
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Al
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622dc354e7
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[optimization] Adding learning rate to lazy sparse update in stochastic gradient descent
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2016-01-12 11:04:16 -05:00 |
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Al
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7cc201dec3
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[optimization] Moving gamma_t calculation to the header in SGD
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2016-01-11 16:40:50 -05:00 |
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Al
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b85e454a58
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[fix] var
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2016-01-09 03:43:53 -05:00 |
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Al
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62017fd33d
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[optimization] Using sparse updates in stochastic gradient descent. Decomposing the updates into the gradient of the loss function (zero for features not observed in the current batch) and the gradient of the regularization term. The derivative of the regularization term in L2-regularized models is equivalent to an exponential decay function. Before computing the gradient for the current batch, we bring the weights up to date only for the features observed in that batch, and update only those values
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2016-01-09 03:37:31 -05:00 |
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Al
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8b70529711
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[optimization] Stochastic gradient descent with gain schedule a la Leon Bottou
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2016-01-08 00:54:17 -05:00 |
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