Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
courses:rg:2012:distributed-perceptron [2012/12/16 17:08] machacek |
courses:rg:2012:distributed-perceptron [2012/12/16 23:44] (current) machacek |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| - | ====== Distributed Training Strategies for the Structured Perceptron - RG report ====== | + | ====== Distributed Training Strategies for the Structured Perceptron - RG report |
| + | |||
| + | ===== Presentation ===== | ||
| + | |||
| + | ==== 3 Structured Perceptron ==== | ||
| + | |||
| + | * 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:// | ||
| + | |||
| + | * 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? | ||
| + | |||
| + | ==== 4 Distributed Structured Perceptron ==== | ||
| + | |||
| + | * Motivation: There is no straightforward way to make the standard perceptron algorithm parallel. | ||
| + | |||
| + | === 4.1 Parameter Mixing === | ||
| + | |||
| + | * 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 | ||
| + | |||
| + | === 4.2 Iterative Parameter Mixing === | ||
| + | |||
| + | ==== 5 Experiments ==== | ||
| + | |||
| ===== Questions ===== | ===== Questions ===== | ||
| Line 38: | Line 71: | ||
| w = [0, 0.6] | w = [0, 0.6] | ||
| + | |||
| + | | ||
| ==== Question 2 ==== | ==== Question 2 ==== | ||
| Line 79: | Line 114: | ||
| N = argmax_N f(N, T, F, ...) | N = argmax_N f(N, T, F, ...) | ||
| f = ? | f = ? | ||
| + | |||
| + | **Answer:** | ||
| + | |||
| + | 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. | ||