Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-15T22:14:59.087555Z
Paper Citation Record · LEDGER
As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2604.20852.
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 2026-05-15T22:14:59.087555Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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
54 of 54 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation fab516cc-118a-4959-96e1-c5f656e2cd62 · outbound
DenoiseRank: Learning to Rank by Diffusion Models In Proceedings of the 22nd international conference on Machine learning, pages 89–96
Reference 1
Source-reported events for the cited work
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Observation 0e939a53-8d41-4274-ab01-7b2e92f7ee53 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Jonathan Ho, Ajay Jain, and Pieter Abbeel
Reference 2
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 1ce78375-29b9-47b4-a570-79bab6e14c00 · outbound
DenoiseRank: Learning to Rank by Diffusion Models MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
Reference 3
Source-reported events for the cited work
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Observation bd655dab-ad7d-4dab-a49e-80ffeaa9ef42 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Wasserstein Generative Learning of Conditional Distribution
Reference 4
Source-reported events for the cited work
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Observation c93df18f-0b4e-470b-82dc-909a50466a2e · outbound
DenoiseRank: Learning to Rank by Diffusion Models ROIC-DM: Robust Text Inference and Classification via Diffusion Model
Reference 5
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Observation 23ccbd3a-08ca-4f7c-9bcc-29afc845d091 · outbound
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Reference 6
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Observation 1a220bd7-e012-46c9-9b61-c5fc6f4e7e4b · outbound
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Reference 7
Source-reported events for the cited work
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Observation 70cf7c7f-8938-4bb8-b5e3-1af36cefbead · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 8
Source-reported events for the cited work
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Observation 3374d26d-c277-4316-8041-4a6a20f3351f · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 9
Source-reported events for the cited work
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Observation 1f95cb00-c185-4a31-bb15-9ec76e9666ef · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 10
Source-reported events for the cited work
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Observation 896a99e0-4bb6-4228-81f8-c4f20d6a6ce0 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 11
Source-reported events for the cited work
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Observation 38e24c8d-4460-434d-9c62-69ccaa811d0f · outbound
DenoiseRank: Learning to Rank by Diffusion Models We have proposed two hypotheses: (1) DenoiseRank demonstrates greater robustness for sparse data (with few effec- tive features)
Reference 12
Source-reported events for the cited work
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Observation 5b68602b-5058-4b81-a650-05866147d7d1 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Effec- tive feature count ranking: YAHOO > ISTLLA > WEB30K
Reference 13
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 3af22ef8-412f-4b15-a8a8-901a66bfa19b · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 14
Source-reported events for the cited work
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Observation fbcf4a39-088b-4259-9657-289f4bafa412 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 15
Source-reported events for the cited work
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Observation 20827002-dcdd-4406-9a30-a8327a386105 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 16
Source-reported events for the cited work
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Observation ffcc1db0-d742-4957-be12-5e7d31965062 · outbound
DenoiseRank: Learning to Rank by Diffusion Models In contrast, query-document length in Istella presents a hump distribution (max length < 190), and those in Yahoo gradually decreases be- tween 1 and 120 (max length < 140)
Reference 17
Source-reported events for the cited work
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Observation 623f3f3e-6688-4890-8351-1ec0492b1ad0 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 18
Source-reported events for the cited work
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Observation 1e4e134e-3257-4887-90fb-0cca5516c6a3 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 19
Source-reported events for the cited work
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Observation 0416ed15-a309-48d4-affe-c337c4191828 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 20
Source-reported events for the cited work
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Observation 52dfe235-876c-42d8-841b-16d46a9a7d3d · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 21
Source-reported events for the cited work
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Observation 57db54ee-5615-4fa4-be72-3856c2db3dc1 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 22
Source-reported events for the cited work
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Observation b7f3ede0-a820-4be9-b240-667106f2a3c0 · outbound
DenoiseRank: Learning to Rank by Diffusion Models The model per- forms optimally on the Istella dataset at T=
Reference 23
Source-reported events for the cited work
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Observation 868465c3-e9ae-4215-8bdf-8dc6a66ae470 · outbound
DenoiseRank: Learning to Rank by Diffusion Models E.2 Noise Scheduler The noise scheduler is the way in which the αt changes during diffusion, where αt := Qt s=1αs, see eq
Reference 24
Source-reported events for the cited work
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Observation f20f6bdf-11f6-4210-8bab-79293936f0ca · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 25
Source-reported events for the cited work
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Observation 9b2f22e8-c429-401b-8b52-ebaa24081256 · outbound
DenoiseRank: Learning to Rank by Diffusion Models TruncatedLinear>Sqrt>Linear>Cosine
Reference 26
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation dff9dc43-0420-48ea-a003-d4116c70fc39 · outbound
DenoiseRank: Learning to Rank by Diffusion Models E.3 The Number of Denoise Network Layers As shown in Figure
Reference 27
Source-reported events for the cited work
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Observation 667068e4-d42a-40dc-897c-642bec7f243a · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 28
Source-reported events for the cited work
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Observation 56bbb547-6295-4dcc-9f1a-d806113e07d4 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 29
Source-reported events for the cited work
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Observation 52ece416-f343-4496-bda1-29d8ce7c5c44 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 30
Source-reported events for the cited work
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Observation 8a5f3096-2420-4c3a-b7ab-bf44418e888a · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 31
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Observation 51b07e06-f657-43ab-a185-f39f1df58f65 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 32
Source-reported events for the cited work
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Observation a6bacd94-77f8-4aa5-bccc-61a04e5df623 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 33
Source-reported events for the cited work
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Observation e9ce06f2-89fb-4ca0-9f79-3cfc5f9186c5 · outbound
DenoiseRank: Learning to Rank by Diffusion Models This is a way to boost premium content exposure
Reference 34
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation da41b377-060c-490e-a022-1e18deed8cc0 · outbound
DenoiseRank: Learning to Rank by Diffusion Models For example
Reference 35
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 1524b14e-8f1a-4093-91f2-1498b9435151 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 36
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4ad5803c-da91-4051-8a07-1ce319414b3b · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unfortunately, previous LTR models did not con- sider uncertainty for ranking and may not rank items diversely
Reference 37
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 1f192383-9ae4-4ac6-9fa1-56e97b20bca7 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Best performance per column in bold
Reference 38
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation e84254ba-915c-482a-aab6-44e0bdd5e051 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 39
Source-reported events for the cited work
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Observation 1213d8ba-c5ac-4ce7-8029-4d87d2ac7b12 · outbound
DenoiseRank: Learning to Rank by Diffusion Models It proved our extrapolate that traditional LTR models do not inject un- certainty which results in a static ranking se- quence
Reference 40
Source-reported events for the cited work
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Observation 1d13e5db-93a3-4e1b-9e8d-886197952f08 · outbound
DenoiseRank: Learning to Rank by Diffusion Models According to the above analysis, our DenoiseR- ank can be applied to areas requiring diverse rank- ing sequences of items
Reference 41
Source-reported events for the cited work
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Observation 63b5e1a9-ccf9-420c-a592-0f73b672cdb4 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 42
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Observation 76cc66b0-b81c-43fb-bf08-df6353e7a051 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 43
Source-reported events for the cited work
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Observation 206e7d60-c4da-45d2-a252-c72a0d76fdf4 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 44
Source-reported events for the cited work
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Observation 0a3d1031-f865-4518-8078-11b21e4194c4 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 45
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 5b42df67-eb26-42c2-8458-1f5042a6a35b · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 46
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation adcaf187-05bc-4564-91fd-bec8c770431b · outbound
DenoiseRank: Learning to Rank by Diffusion Models For different loss functions, we use AdamW optimizer and scan learning rate∈0.01,0.001,0.0001
Reference 47
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 6337a443-50a8-40b5-bf9b-df604a909161 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 48
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 0d310615-3701-4d1f-b377-e9cf539f72b6 · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 49
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation a4267807-4c85-4912-b320-fa1152539b1d · outbound
DenoiseRank: Learning to Rank by Diffusion Models How- ever, for the Yahoo! and Istella datasets, train- ing with MSE loss is the best choice
Reference 50
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4f8813f1-caa4-486a-8e49-5c457f7b61ff · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 51
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 716d9035-8bff-4741-be18-faf25d0c322c · outbound
DenoiseRank: Learning to Rank by Diffusion Models Therefore, it is necessary to strike a balance between time and performance and to use the appropriate time step in practice
Reference 52
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation b7fc70cb-95a3-4656-8e2e-585134c9d30e · outbound
DenoiseRank: Learning to Rank by Diffusion Models Unresolved cited work
Reference 53
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 98024416-be1e-4be6-b8c3-2388d75b916f · outbound
DenoiseRank: Learning to Rank by Diffusion Models The time processed by the tree- based models is significantly lower than that by the neural network-based models, which is due to their more light-weight model struc- ture and size
Reference 54
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
No inbound Pith citation observations are available.