Papers with compressed embeddings
Knowledge Graph Embedding Compression (2020.acl-main)
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| Challenge: | Knowledge graph (KG) embedding techniques that learn continuous embedds of entities and relations consume a large amount of storage and memory. |
| Approach: | They propose a method that compresses the KG embedding layer by representing each entity in the KA as a vector of discrete codes and then composes the embeddables from these codes. |
| Outcome: | The proposed approach achieves 50-1000x compression of embeddings with a minor loss in performance on standard KG embeddable evaluations and retains the ability to perform reasoning tasks such as KG inference. |
Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings (2025.findings-emnlp)
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| Challenge: | Existing evaluation methods for compressed text embeddings are either expensive or too simplistic. |
| Approach: | They propose a task-agnostic intrinsic evaluation framework that provides a reliable proxy for downstream performance. |
| Outcome: | The proposed framework provides a reliable proxy for downstream performance. |