Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T10:32:06.482595Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.20214.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T10:32:06.482595Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
45 of 45 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation f2386326-d0ba-4726-a241-6f8e462372f8 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Advances in Neural Information Processing Systems , year=
Reference 1
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Observation a57e2c03-f0e1-4cdb-ae6f-b5a2a738fdb6 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 2
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Observation d1a0e1aa-e3be-4667-8b72-10caf507bacf · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Advances in Neural Information Processing Systems , year=
Reference 3
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Observation 792e0277-ce1a-4aa2-8c6f-2fa4259d1ae5 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers International Conference on Learning Representations , year=
Reference 4
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Observation d5a379ad-7213-44c9-b700-9f8ba08c8911 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers LLaMA: Open and Efficient Foundation Language Models
Reference 5
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Observation 304d326e-f7c3-49b4-aa93-fc09c0dda553 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers and Ermon, Stefano and Rudra, Atri and R
Reference 6
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Observation 2222f6ce-efbc-420d-a4a7-3ee00c44cca5 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Reference 7
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Observation 46e1316b-dbfb-47d7-a57a-a0faddd2fe45 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Generating Long Sequences with Sparse Transformers
Reference 8
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Observation bb86d545-6f8d-41a5-bd9d-6108a4ee656d · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Longformer: The Long-Document Transformer
Reference 9
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Observation 57c2936f-b0ab-4496-9751-3d686217856e · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Advances in Neural Information Processing Systems , year=
Reference 10
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Observation 0c936693-3a75-4612-a334-bdbd97b6659c · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers International Conference on Learning Representations , year=
Reference 11
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Observation 08bbb57f-43a6-4ed8-9875-460c942f739a · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Transactions of the Association for Computational Linguistics , volume=
Reference 12
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Observation cf254f11-37fb-4012-9b70-e96f069ea1d2 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers , journal=
Reference 13
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Observation d8f2b60a-7523-4601-949a-e0b30416d829 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Linformer: Self-Attention with Linear Complexity
Reference 14
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Observation de48ab99-abb4-4d16-bb85-747e0744657c · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Transformers are
Reference 15
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Observation 071f3523-8bfa-4dc4-9d4f-ddbb3ed24dcb · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers International Conference on Learning Representations , year=
Reference 16
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Observation 58c6c581-a104-4fb5-907c-d63a33b8963a · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers International Conference on Learning Representations , year=
Reference 17
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Observation 89088e31-efa1-4264-ad12-6dbffd28cfcb · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 18
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Observation fe80ecdf-fd2b-44b1-a5c8-6ee6aa7b317f · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2026 , url=
Reference 19
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Observation 09dc09e6-a5a9-4bc2-b362-1dfba00172cf · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Long Range Arena: A Benchmark for Efficient Transformers
Reference 20
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Observation 08edee9a-fa1e-41c1-9cfd-ccefd8d3f6d4 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers ACM Computing Surveys , volume=
Reference 21
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Observation 0597e20f-5870-4002-ac2f-fc174fa746e1 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Advances in Neural Information Processing Systems , year=
Reference 22
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Observation 6582d19c-aa01-4a02-b9b9-aad9d92e6258 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Journal of the ACM , volume=
Reference 23
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Observation 53acde4d-0d23-4dfc-a1f0-f896f11c4667 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers SIAM Journal on Optimization , volume=
Reference 24
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Observation ec281921-eccc-494a-99a2-8e00e4119f37 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , booktitle=
Reference 25
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Observation 04b04466-f7ad-47d0-8bad-9df2bafa4b32 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Exploring Low Rank Training of Deep Neural Networks
Reference 26
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Observation 0d84977c-0da4-46c3-b011-aae388d0b3f8 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 27
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Observation 481f74eb-165b-45f1-bebb-6aba7c61eb3c · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 28
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Observation 5955f48f-3cdf-42df-a6b9-b442a2c76806 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 29
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Observation 4ec3404b-1213-4ffd-a4e5-0228f44f470d · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Advances in Neural Information Processing Systems , year =
Reference 30
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Observation 92178cd2-2889-477e-96ce-aef2b6b9878f · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2024 , eprint =
Reference 31
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Observation 57bde0c9-5ef0-470e-9dc8-8fda511f06f0 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2024 , publisher =
Reference 32
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Observation 9eece39f-7468-4cdb-b686-ce195d496009 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers and Li, Dongsheng and Lin, Chin-Yew and Yang, Yuqing and Qiu, Lili , booktitle =
Reference 33
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Observation 38ddc183-5718-47a3-a603-6afc048a6e41 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2023 , publisher =
Reference 34
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Observation c1eb0afa-b30c-49bd-b6dd-754ccc0131fa · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
Reference 35
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Observation c745175a-8bb7-434f-942b-fad0ecafc5de · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics , pages=
Reference 36
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Observation 42ae099a-7178-431d-a5fd-e4c72e06db10 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers European Conference on Computer Vision (ECCV) , pages=
Reference 37
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Observation 04812f29-a30a-4d80-858a-aadd3033d12f · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Reference 38
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Observation ccd33754-2d96-4105-9f55-be8fe7a577ed · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing , year=
Reference 39
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Observation b7a4aabb-6754-43e1-b46a-b6442856f85f · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages=
Reference 40
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Observation ab02ad10-5ab7-4e46-b193-cfb5e5962209 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers arXiv preprint arXiv:2401.XXXX , year=
Reference 41
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Observation 6f2857e7-a857-4688-911a-b3f1915a9dc1 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2023 , publisher=
Reference 42
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Observation 25eaf955-bd09-40fe-9474-d2e8f6b6bca7 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 43
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Observation 59582d41-f65b-42cb-bd30-1933e6b6a49d · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers Unresolved cited work
Reference 44
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Observation 1171c431-d196-4cf7-b483-9cb3cfa84fc8 · outbound
ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers 2023 , eprint =
Reference 45
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No inbound Pith citation observations are available.