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courses:rg:2012:distributed-perceptron [2012/12/16 17:37] machacek |
courses:rg:2012:distributed-perceptron [2012/12/16 23:44] (current) machacek |
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| - | ====== Distributed Training Strategies for the Structured Perceptron - RG report ====== | + | ====== Distributed Training Strategies for the Structured Perceptron - RG report |
| ===== Presentation ===== | ===== Presentation ===== | ||
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| + | ==== 3 Structured Perceptron ==== | ||
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| + | * In unstructured perceptron, you are trying to separate two sets of with hyperplane. See Question 1 for the algorithm. In training phase, you iterate your training data and adjust the hyperplane every time you make a mistake. [[http:// | ||
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| + | * Structured (or multiclass) perceptron is generalization of the unstructured perceptron. See figure 1 in the paper for the algorithm. | ||
| + | * You can use any structured input x (not just vector, sentence for example) and any structured output y (not just binary value, parse tree for example) | ||
| + | * You need to have fuction f(x,y) which returns feature representation of candidate input-output pair | ||
| + | * Using the Theorem 1, you can bound the number of mistakes made during the training | ||
| + | * The computational time is therefore also bounded. | ||
| + | * This holds only for linearly separable sets. | ||
| + | * Other remarks and discussed issues | ||
| + | * The perceptron training algorithm does not always return the same weights (unlike maximal margin). It depends on order of training data. | ||
| + | * How the inference is done in the difficult tasks like parsing? Iterating all possible y? Approximation? | ||
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| + | ==== 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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| + | * First attempt to map-reduce algorithm | ||
| + | * Divide the training data into shards | ||
| + | * In MAP step, train a perceptron on each shard | ||
| + | * In REDUCE step, average the trained weight vectors | ||
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| + | === 4.2 Iterative Parameter Mixing === | ||
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| + | ==== 5 Experiments ==== | ||
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| w = [0, 0.6] | w = [0, 0.6] | ||
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| ==== Question 2 ==== | ==== Question 2 ==== | ||