We lifted 8 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mnick/scikit-kge | pwc_unofficial | 0 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| cconv | Not yet run | mnick/scikit-kge/skge/util.py code served (permissive licence) · get_code("a1ff90bb8c12b9e2") |
| ccorr | Not yet run | mnick/scikit-kge/skge/util.py code served (permissive licence) · get_code("f1c91ce2de33bcff") |
| grad_sum_matrix | Not yet run | mnick/scikit-kge/skge/util.py code served (permissive licence) · get_code("a5d1dd76b77390ab") |
| init_nunif | Not yet run | mnick/scikit-kge/skge/param.py code served (permissive licence) · get_code("f8afbc7c95eb51e8") |
| init_randn | Not yet run | mnick/scikit-kge/skge/param.py code served (permissive licence) · get_code("a590708a90bcec18") |
| init_unif | Not yet run | mnick/scikit-kge/skge/param.py code served (permissive licence) · get_code("9c11469e31d9e3d1") |
| sigmoid | Not yet run | mnick/scikit-kge/skge/base.py code served (permissive licence) · get_code("c50decfd17e5027a") |
| type_index | Not yet run | mnick/scikit-kge/skge/sample.py code served (permissive licence) · get_code("0a1271415524bf30") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1510.04935")
get_code_for_paper("1510.04935")
have("1510.04935")
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