Sign Rank Limitations for Inner Product Graph Decoders
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910452235108352 |
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| author | Lee, Su Hyeong Zhang, Qingqi Kondor, Risi |
| author_facet | Lee, Su Hyeong Zhang, Qingqi Kondor, Risi |
| contents | Inner product-based decoders are among the most influential frameworks used to extract meaningful data from latent embeddings. However, such decoders have shown limitations in representation capacity in numerous works within the literature, which have been particularly notable in graph reconstruction problems. In this paper, we provide the first theoretical elucidation of this pervasive phenomenon in graph data, and suggest straightforward modifications to circumvent this issue without deviating from the inner product framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06662 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sign Rank Limitations for Inner Product Graph Decoders Lee, Su Hyeong Zhang, Qingqi Kondor, Risi Machine Learning Inner product-based decoders are among the most influential frameworks used to extract meaningful data from latent embeddings. However, such decoders have shown limitations in representation capacity in numerous works within the literature, which have been particularly notable in graph reconstruction problems. In this paper, we provide the first theoretical elucidation of this pervasive phenomenon in graph data, and suggest straightforward modifications to circumvent this issue without deviating from the inner product framework. |
| title | Sign Rank Limitations for Inner Product Graph Decoders |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.06662 |