Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T11:40:33.734051Z
Paper Citation Record · LEDGER
As of 6 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.05417.
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 2026-07-12T11:40:33.734051Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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 dff54fd6-57b1-4e01-9457-1ae2fc33dd2a · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Coding theorems for a discrete source with a fidelity criterion,
Reference 1
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Observation 0ef6ac53-3979-4d69-b790-f5293d9e7e88 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems The information bottleneck method,
Reference 2
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Observation 8687d20e-1572-471a-8dd1-3e4ce16889ad · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Information transmission with addi- tional noise,
Reference 3
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Observation ebb64599-6c56-49ee-94e1-b70663d548c6 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems End-to-end optimized image compression,
Reference 4
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Observation 84b8c617-058e-48b2-b68d-f6d00aa52848 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Deep learning and the information bot- tleneck principle,
Reference 5
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Observation ce754e52-b212-4442-af0c-1acd4344a5cf · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Computation of channel capacity and rate-distortion func- tions,
Reference 6
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Observation 2ac9e28f-caf2-490b-a0cb-e6282912dc6e · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems An algorithm for computing the capacity of arbitrary dis- crete memoryless channels,
Reference 7
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Observation ad736afb-2e30-42ae-a397-050639fab312 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Bregman divergence based EM algorithm and its applica- tion to classical and quantum rate distortion theory,
Reference 8
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Observation b18ba8f1-84f0-4d63-acc5-6de056efc654 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems A con- strained BA algorithm for rate-distortion and distortion-rate functions,
Reference 9
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Observation e8589800-d6df-469e-acca-4b13308075a7 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems A communi- cation optimal transport approach to the computation of rate distortion functions,
Reference 10
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Observation bc8a0fd2-f2a5-4373-addf-04073bb46cae · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Towards empirical sandwich bounds on the rate-distortion function,
Reference 11
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Observation 302a2c8c-81b2-42dd-9bbd-bc748ae0e733 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Neural estimation of the rate- distortion function with applications to operational source coding,
Reference 12
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Observation 19ff15bb-d86d-4a4e-9b46-0c2267e89ae4 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Mutual information neural estimation,
Reference 13
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Observation 0fa5bb5e-fc45-4ccd-80d2-0fc987b8819e · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Auto-encoding variational bayes,
Reference 14
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Observation 84051066-d5b9-4c6c-b778-17aa3194bfc9 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Generative adversarial nets,
Reference 15
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Observation 84382c55-a4bd-4c83-a327-8624e7f3b0de · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Beyond normal: On the evaluation of mutual information estimators,
Reference 16
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Observation 903fedba-5aad-426b-8acc-ba9e500f5b53 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems The information bottleneck problem and its applications in machine learning,
Reference 17
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Observation 21acdb27-ca96-4e74-8b78-0a23a41e2aeb · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems An introduction to neural data compression,
Reference 18
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Observation b10022cc-b1bc-456c-b5b2-5275d83bcfb3 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Deep variational information bottleneck,
Reference 19
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Observation eb8a1b9c-19cc-402e-8573-6c49ecf77222 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Rate distortion via constrained estimated mutual information minimization,
Reference 20
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Observation 95eaea6e-3e37-494a-9476-a9062619c31d · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Adversarial information bottleneck,
Reference 21
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Observation ee22864f-15f6-4770-9a35-5b1344125747 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Neural estimation of the information bottleneck based on a mapping approach,
Reference 22
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Observation 0e3f74ba-c8fa-481b-be8d-4fb01f5db6de · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Data-driven neural estimation of indirect rate-distortion function,
Reference 23
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Observation b2b81a48-6c24-492f-a570-7fb5cd525b8d · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Rate-distortion theory,
Reference 24
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Observation 1605b392-2d27-4bcb-bcc3-ed36ed191679 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Indirect rate distortion problems,
Reference 25
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Observation 4afbe071-4ef0-49c6-a047-22810af421b0 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Wasserman,All of nonparametric statistics
Reference 26
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Observation c26f73a1-d078-4cad-88d1-9a7be99cab0f · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Stochastic backpropaga- tion and approximate inference in deep generative models,
Reference 27
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Observation 05e2a621-4178-4fd2-bc72-beab94240e08 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems beta-V AE: Learning basic visual concepts with a constrained variational framework
Reference 28
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Observation 6010abab-4c3d-48eb-a95e-ee6db47ba122 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems U-net: Convolutional networks for biomedical image segmentation,
Reference 29
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Observation 554716da-057f-49c5-a5d7-6122e68c2357 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Variational inference with normalizing flows,
Reference 30
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Observation 92957421-d44a-4044-941d-d08907abe869 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Multi-rate V AE: Train once, get the full rate-distortion curve,
Reference 31
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Observation 8b41dd46-bedf-4752-9cca-d88b9876be5e · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Information dropout: Learning optimal rep- resentations through noisy computation,
Reference 32
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Observation 793cc835-eb92-4817-9bf9-2854898dc619 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Nonlinear information bottleneck,
Reference 33
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Observation 4e08247f-1fa8-43a8-8865-95bfe753690a · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Asymptotic evaluation of certain Markov process expectations for large time, I,
Reference 34
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Observation 7cf7482d-6c8a-4deb-b863-ade07f6ef5fe · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Source coding, large deviations, and approximate pattern matching,
Reference 35
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Observation 26d9d411-9743-4601-8898-8f720b0f4b89 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems A mapping approach to rate-distortion computation and analysis,
Reference 36
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Observation 538829d0-b67d-479f-acdb-bb88d991d56f · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Rate-distortion theory by and for energy-based models,
Reference 37
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Observation 2191031d-359c-4dd8-ab11-768f95429e78 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Estimating rate-distortion functions using the energy-based model,
Reference 38
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Observation 4ace6b1c-d45e-45f5-98c6-85ec3620b374 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Correlation functions and computer simulations,
Reference 39
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Observation c5dcc4d1-327e-43cc-838f-912f837c83a4 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Unresolved cited work
Reference 40
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Observation fb6bb552-a956-46b9-a0d7-e0a25ee20fdf · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Caveats for information bottleneck in deterministic scenarios,
Reference 41
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Observation 19c85fc8-5c4c-4241-b778-e5e6dd414c69 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems On variational bounds of mutual information,
Reference 42
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Observation f6275763-ccc3-407b-8e09-c36468dad79d · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Denoising diffusion probabilistic models,
Reference 43
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Observation 98bed057-bcaf-4fdc-83f3-300a9a3fca8c · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Score-based generative modeling through stochastic differen- tial equations,
Reference 44
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Observation 62b72fe7-840d-429b-90dc-f9f9737ae1f4 · outbound
A Survey of Learn-to-Compute Paradigms for Rate-Distortion-Type Problems Georgii,Gibbs Measures and Phase Transitions
Reference 45
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No inbound Pith citation observations are available.