Papers by Albert Sayapin
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering (2022.acl-srw)
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| Challenge: | Existing methods to embed learning use a standard Neural Networks (NN) backward mechanism, duplicating its memory consumption. |
| Approach: | They propose a memory-efficient KG embedding model that embeds knowledge graphs as 3rd-order binary tensors. |
| Outcome: | The proposed model yields comparable performance on link prediction and KG-based question answering tasks. |