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courses:rg:2012:distributed-perceptron [2012/12/16 16:46] machacek |
courses:rg:2012:distributed-perceptron [2012/12/16 21:07] machacek |
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====== Distributed Training Strategies for the Structured Perceptron - RG report ====== | ====== Distributed Training Strategies for the Structured Perceptron - RG report ====== | ||
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+ | ===== Presentation ===== | ||
+ | |||
+ | ==== 3 Structured Perceptron ==== | ||
+ | |||
+ | ==== 4 Distributed Structured Perceptron ==== | ||
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+ | * Motivation: There is no straightforward way to make the standard perceptron algorithm parallel. | ||
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+ | === 4.1 Parameter Mixing === | ||
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+ | === 4.2 Iterative Parameter Mixing === | ||
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+ | ==== 5 Experiments ==== | ||
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===== Questions ===== | ===== Questions ===== | ||
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==== Question 3 ==== | ==== Question 3 ==== | ||
In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)? | In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)? | ||
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+ | |||
+ | **Answer:** | ||
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+ | * Iterative Parameter Mixing is just a form of parameter averaging, which has the same effect as the averaged perceptron. | ||
+ | * F-measures for seral (All Data) and Paralel (Iterative Parameter Mix) are very similar in the second column. It is because the both methods are already averaged. | ||
+ | * Bagging like effect | ||
==== Question 4 ==== | ==== Question 4 ==== | ||
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N = argmax_N f(N, T, F, ...) | N = argmax_N f(N, T, F, ...) | ||
f = ? | f = ? | ||
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+ | **Answer:** | ||
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+ | We have not concluded on a particular formula. | ||
+ | * It also depends on convergence criteria. | ||
+ | * With no time limitation, the serial algorithm would have the least energy consumption. | ||
+ | * With time limitation, we should use as least shards to meet the time limitation. |