Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:03:33.738965Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2507.17221.
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 2026-08-06T15:03:33.738965Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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
65 of 65 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 76622ebc-5c23-465b-a5c4-367fb7f4be7e · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Soft-to-hard vector quantization for end-to-end learn- ing compressible representations
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Reference 3
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Dataset Distillation as Data Compression: A Rate-Utility Perspective Simoncelli
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Observation d7106b51-2717-4bbd-86d9-2c1d9456476a · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al
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Dataset Distillation as Data Compression: A Rate-Utility Perspective Efros, and Jun-Yan Zhu
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Observation ea969c11-55b9-4b37-b777-7749a296ab9b · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Efros, and Jun-Yan Zhu
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Observation d704132f-0f98-4888-afdf-93997557b9d6 · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Influence-guided diffusion for dataset distillation
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Observation 8d0a4f3f-8a0a-4e43-b229-2cb76826c40b · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Cover and Joy A
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Observation 443d7f03-cf85-42c1-bc3a-cdbf15fa34fb · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Scaling up dataset distillation to ImageNet-1K with constant mem- ory
Reference 13
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Observation 30b8b0ef-6c86-4f5e-bef8-8c7fc02a83de · outbound
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Dataset Distillation as Data Compression: A Rate-Utility Perspective NICE: Non-linear independent components estimation
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Observation f4f9753f-67fd-46f9-b385-39c67c8827fd · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective An image is worth 16x16 words: Trans- formers for image recognition at scale
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Observation bad1eb88-6c11-48e3-9332-3d260a0ce3bc · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Embarrassingly simple dataset distillation
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Observation 5dba72af-6b6c-4142-a68d-164dad95d201 · outbound
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Observation 904d178b-be16-4f0b-b10b-03ab6e16e0d0 · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Borgwardt, Malte J
Reference 20
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Observation 7cfae8e4-38a5-4cd9-aef1-44edbd2c1412 · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Deep residual learning for image recognition
Reference 22
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Dataset Distillation as Data Compression: A Rate-Utility Perspective Deep Learning Scaling is Predictable, Empirically
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Observation b59ff9ff-4461-43db-a7f6-3ed41553690f · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Unresolved cited work
Reference 24
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Dataset Distillation as Data Compression: A Rate-Utility Perspective Scaling Laws for Neural Language Models
Reference 25
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Observation f28a08cb-6c2c-48cb-916d-06c9955465fb · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Schwarz, and Emilien Dupont
Reference 26
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Observation f422d1df-b88e-4eb3-bd4b-e51b95f5099c · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Dataset condensation via efficient synthetic- data parameterization
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Observation 44eb2070-c132-4602-ac8b-d2d6135a68cb · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Kingma and Max Welling
Reference 28
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Dataset Distillation as Data Compression: A Rate-Utility Perspective Unresolved cited work
Reference 29
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Dataset Distillation as Data Compression: A Rate-Utility Perspective COOL-CHIC: Coordinate-based low complexity hierarchical image codec
Reference 30
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Observation ac3dc5f9-73ed-403b-b53c-71f69a1502a9 · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective A comprehensive survey of dataset distillation
Reference 31
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Observation 24d1ca0e-00d0-427d-bc24-5fa82d214c86 · outbound
Dataset Distillation as Data Compression: A Rate-Utility Perspective Efficient and effective context-based convolutional en- tropy modeling for image compression
Reference 32
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Dataset Distillation as Data Compression: A Rate-Utility Perspective The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
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Observation c1130411-7bfb-40b2-a14c-95d6040ef8d0 · outbound
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Reference 36
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Reference 37
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Reference 40
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Observation c3c3e498-d6cd-4d12-8d9a-5f1157122710 · outbound
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Reference 42
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Dataset Distillation as Data Compression: A Rate-Utility Perspective RNADE: The real-valued neural autoregressive density-estimator
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Reference 48
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Reference 50
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Reference 51
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Reference 52
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Reference 53
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Reference 54
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Reference 55
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Reference 62
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Reference 63
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Reference 65
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No inbound Pith citation observations are available.