This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
en:dydaktyka:problog:lab3 [2019/01/14 13:40] msl [Toy Problem] |
en:dydaktyka:problog:lab3 [2019/01/14 15:49] msl [Structure Learning] |
||
|---|---|---|---|
| Line 23: | Line 23: | ||
| ===== Toy Problem ===== | ===== Toy Problem ===== | ||
| - | {{ :en:dydaktyka:problog:toy_link_1.png?200|}}Let assume we have a very tiny network as shown on the right. In this problem all links are undirected and unlabeled. Nodes have labels shown using different colors. | + | {{ :en:dydaktyka:problog:toy_link_1.png?200|}}Let assume we have a very tiny network, similar to the one shown on the right. In this problem all links are undirected and unlabeled. Nodes have labels shown using different colors. |
| Our ask is to train a link predictor using [[https://dtai.cs.kuleuven.be/problog/|Problog]]. In case somebody forgot Problog installation is fairly easy given a working Python environment (''pip install problog'' and optionally ''problog install'' on Linux). In case it wasn't simple enough, one can try to use the [[https://dtai.cs.kuleuven.be/problog/editor.html|on-line interface]]. The evidence file for the problem can downloaded from {{ :en:dydaktyka:problog:link_prediction_data.pl | this link}}. | Our ask is to train a link predictor using [[https://dtai.cs.kuleuven.be/problog/|Problog]]. In case somebody forgot Problog installation is fairly easy given a working Python environment (''pip install problog'' and optionally ''problog install'' on Linux). In case it wasn't simple enough, one can try to use the [[https://dtai.cs.kuleuven.be/problog/editor.html|on-line interface]]. The evidence file for the problem can downloaded from {{ :en:dydaktyka:problog:link_prediction_data.pl | this link}}. | ||
| + | You can start from {{ :en:dydaktyka:problog:link_prediction_empty.pl |this point}}. | ||
| == Questions: == | == Questions: == | ||
| Line 81: | Line 82: | ||
| == Questions: == | == Questions: == | ||
| - | | + | |
| - What applications of structure learning can you imagine? | - What applications of structure learning can you imagine? | ||
| - Do you know any related problems/methods? | - Do you know any related problems/methods? | ||
| Line 176: | Line 177: | ||
| example_mode(auto). | example_mode(auto). | ||
| </code> | </code> | ||
| + | |||
| + | ===== Big Fat Assignment ====== | ||
| + | |||
| + | - Try to learn structure of the Information Retrieval model, you've done earlier by hand. | ||
| + | - Is the learned model satisfying? If not, what is the problem? Try to fix it by changing learning data by hand. | ||
| + | - Modify model to consider more than only one query. What has to be changed? | ||
