Dongxu Zhang - 版本历史

2016年2月22日 (一) 00:09 Zhangdx

2016-02-22T00:09:45Z

2015年10月5日 (一) 13:19 Zhangdx

2015-10-05T13:19:22Z

2015年10月5日 (一) 13:17 Zhangdx

2015-10-05T13:17:59Z

2015年10月5日 (一) 13:14 Zhangdx

2015-10-05T13:14:43Z

2015年10月5日 (一) 13:10 Zhangdx

2015-10-05T13:10:10Z

Zhangdx：以“Last Week: * Did following experiments: * pooling method.(have run 195 hours for 117 iterations. performance is still raising(0.49). but haven't exceeded transE...”为内容创建页面

2015-10-05T13:09:04Z

以“Last Week: * Did following experiments: * pooling method.(have run 195 hours for 117 iterations. performance is still raising(0.49). but haven't exceeded transE...”为内容创建页面

新页面

Last Week:
* Did following experiments:
* pooling method.(have run 195 hours for 117 iterations. performance is still raising(0.49). but haven't exceeded transE yet.(0.53), one iteration takes 1h40min )
* pooling method with sampling method and minibatch.(performance grew very slowly after several iteration.(0.37) It seems that sampling is not as good as origin pooling method. one iteration takes 40 minutes)
* attention pooling method.(very slow, one iteration may take two days or more. still waiting for the performance of first iteration.)
* attention pooling method with sampling and minibatch.( still running. one iteration takes 1 hour.)

This Week:
* waiting for results and also think about other improvements.
* pooling method with entity and relation is too slow. Another simplification is to ignore the around entities during pooling, only relation types left, which leads to a feature vector joining behind the orginal entity vector, describing the type of entity. This is more like the original transE.
* I feel that the chain rule should based on random walk. Mean pooling leads to equal weights. But random walk give high possibility to high page rank entities. So there should be improvements.

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 Last Week:
-* Did following experiments:
+----
-    * pooling method.(have run 195 hours for 117 iterations. performance is still raising(0.49). but haven't exceeded transE yet.(0.53), one iteration takes 1h40min )
+* Read Tensorflow guidebook.
-    * pooling method with sampling and minibatch.(performance grew very slowly after several iteration.(0.37) It seems that sampling is not as good as origin pooling method.
+* Run tensorflow's translation code.
-      And because cost stops going down when it is still a quite big value, which means sampling may lead to a saddle point? one iteration takes 40 minutes)
-    * attention pooling method.(very slow, one iteration may take two days or more. still waiting for the performance of first iteration.)
-    * attention pooling method with sampling and minibatch.( still running. one iteration takes 1 hour.)
 This Week:
-* waiting for results and also think about other improvements.
+----
-* pooling method with entity and relation is too slow. Another simplification is to ignore the around entities during pooling, only relation types left, which leads to a feature vector joining behind the orginal entity vector, describing the type of entity. This is more like the original transE.
+* Read the translation code and print alignment information.
-* I feel that the chain rule should based on random walk. Mean pooling leads to equal weights. But random walk give high possibility to high page rank entities. So there should be improvements.
+* Try to hack into the code and change the structure.