Paper Citation Record · LEDGER

Unsupervised Neural Quantization for Compressed-Domain Similarity Search

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.03883.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.03883 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:06:33.827977Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy25
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbd09b00-5a34-40bc-9014-2656b54b911e · outbound

This paper cites Soft-to-hard vector quantization for end-to-end learn- ing compressible representations.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Soft-to-hard vector quantization for end-to-end learn- ing compressible representations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.443596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f4cdc1d1-e15d-4ec3-b35e-a9c20f34c61b · outbound

This paper cites Lempitsky.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Lempitsky

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.428598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f6759f7b-f2fe-4297-830f-e793bd56f8e1 · outbound

This paper cites Lempitsky.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Lempitsky

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.412968Z

Source-reported events for the cited work

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Observation 295d4a54-ef70-400e-ba94-52a4050f433e · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Deep clustering for unsupervised learning of visual features

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.397122Z

Source-reported events for the cited work

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Observation 351cca41-a4d2-463e-b72f-48edb7761e9e · outbound

This paper cites Approximate nearest neighbor search by residual vector quantization.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Approximate nearest neighbor search by residual vector quantization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.381878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5bfa107c-b0df-45ac-9a65-2f8133883314 · outbound

This paper cites Opti- mized product quantization for approximate nearest neigh- bor search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Opti- mized product quantization for approximate nearest neigh- bor search

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.365829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3555b565-2a14-4a97-8d2c-94264cbd86c9 · outbound

This paper cites Iterative quantization: A procrustean approach to learning binary codes for large-scale im- age retrieval.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Iterative quantization: A procrustean approach to learning binary codes for large-scale im- age retrieval

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.350582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29e743c6-9792-4c99-b84b-8f62c2aec549 · outbound

This paper cites Compressing Deep Convolutional Networks using Vector Quantization.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Compressing Deep Convolutional Networks using Vector Quantization

Reference 8

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Source-reported events for the cited work

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Observation 201ad19c-c951-49f6-b9da-8e24706a7735 · outbound

This paper cites Spherical hashing.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Spherical hashing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.335857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a44806f-d9b2-4ce9-9c2b-e0322a02f1df · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal co- variate shift.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Batch normalization: Accelerating deep network training by reducing internal co- variate shift

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.320961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e6215d7-4d10-4666-93db-70a74a2d1cbd · outbound

This paper cites Subic: A supervised, structured binary code for image search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Subic: A supervised, structured binary code for image search

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.304884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef70a119-d338-4614-ad94-5da4617978c0 · outbound

This paper cites Categorical repa- rameterization with gumbel-softmax.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Categorical repa- rameterization with gumbel-softmax

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.289746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd7d6319-50f0-4612-8098-c009863d4300 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Categorical Reparameterization with Gumbel-Softmax

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.718831Z

Source-reported events for the cited work

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Observation 58cac3c5-3519-4df1-91a1-ceadac7b820a · outbound

This paper cites Prod- uct quantization for nearest neighbor search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Prod- uct quantization for nearest neighbor search

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.274588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d5a42bb5-65af-4928-a9b3-26fb4aa5a1e4 · outbound

This paper cites Searching in one billion vectors: Re-rank with source coding.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Searching in one billion vectors: Re-rank with source coding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.258301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 529496c9-4662-4a76-945f-165a819c42c0 · outbound

This paper cites Billion-scale similarity search with GPUs.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Billion-scale similarity search with GPUs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.732489Z

Source-reported events for the cited work

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Observation 270e4d82-7b66-42e7-9da9-b0f7665f1a77 · outbound

This paper cites Fast de- coding in sequence models using discrete latent variables.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Fast de- coding in sequence models using discrete latent variables

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.242719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6c1e0c8c-4f63-40c9-83bb-a05877614619 · outbound

This paper cites End-to-End Supervised Product Quantization for Image Search and Retrieval.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search End-to-End Supervised Product Quantization for Image Search and Retrieval

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:06:33.965337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a8f1a91-07e6-4b08-839b-0e279a4ee508 · outbound

This paper cites Hesch, Marc Pollefeys, and Roland Siegwart.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Hesch, Marc Pollefeys, and Roland Siegwart

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.226015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d568a897-94ad-4452-87b1-d003610eba3d · outbound

This paper cites Quasi-hyperbolic momentum and Adam for deep learning.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Quasi-hyperbolic momentum and Adam for deep learning

Reference 20

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Source-reported events for the cited work

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Observation 79f25d47-5b64-43c1-8e38-6cea397c30bc · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.754879Z

Source-reported events for the cited work

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Observation 16bc3510-9802-4d5d-b602-959163d3566b · outbound

This paper cites Hoos, and James J.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Hoos, and James J

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.210665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dfe59d60-5419-4b4c-9bea-6eb205a5cb68 · outbound

This paper cites Hoos, and James J.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Hoos, and James J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.195886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1d107327-32b1-43ec-9e36-c8218d4a0802 · outbound

This paper cites an unresolved cited work.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:06:34.180290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 73736634-c0bb-4843-a584-3c5f0df66b9b · outbound

This paper cites Competitive quantization for approximate nearest neighbor search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Competitive quantization for approximate nearest neighbor search

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.163701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 78f1c65c-e51b-412a-a1d4-f826c6321b16 · outbound

This paper cites Spreading vectors for similarity search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Spreading vectors for similarity search

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.147328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2d3956d1-4dad-4158-9203-d485b51aa6f0 · outbound

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Unsupervised Neural Quantization for Compressed-Domain Similarity Search Unresolved cited work

Reference 27

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Source-reported events for the cited work

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Observation 31c0e4c8-61fe-4ecf-b00f-b563a2655a47 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 28

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Source-reported events for the cited work

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Observation d490a9d5-fa5f-427f-9026-850f5d45def3 · outbound

This paper cites Compressing Word Embeddings via Deep Compositional Code Learning.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Compressing Word Embeddings via Deep Compositional Code Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.791034Z

Source-reported events for the cited work

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Observation d55ec495-5714-4245-89ec-3bded58b75d0 · outbound

This paper cites Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.795895Z

Source-reported events for the cited work

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Observation dc9ce36a-111a-4c06-aaa1-4b740e816d44 · outbound

This paper cites Maddison, John Law- son, and Jascha Sohl-Dickstein.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Maddison, John Law- son, and Jascha Sohl-Dickstein

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.114554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation abb51a58-2e3f-4ce9-a403-c7c5e2102d8d · outbound

This paper cites Neural discrete representation learning.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Neural discrete representation learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.096846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8e9bf33b-e3a9-4316-af39-4f48df169c97 · outbound

This paper cites Spec- tral hashing.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Spec- tral hashing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.081072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1b0c4b53-9e8a-4d5d-aa57-c50a3a06a7cb · outbound

This paper cites Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-14T14:06:33.813706Z

Source-reported events for the cited work

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Observation b5500b42-e33a-4918-a683-9c9ec0f6f635 · outbound

This paper cites Product quantization network for fast image retrieval.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Product quantization network for fast image retrieval

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.065357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 21e05616-c091-4b63-990c-5d580c4d3397 · outbound

This paper cites PQ-CNN: accelerating prod- uct quantized convolutional neural network on FPGA.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search PQ-CNN: accelerating prod- uct quantized convolutional neural network on FPGA

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.049800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 48abf67e-3479-4ee1-a92e-78163e721bf9 · outbound

This paper cites Composite quan- tization for approximate nearest neighbor search.

Unsupervised Neural Quantization for Compressed-Domain Similarity Search Composite quan- tization for approximate nearest neighbor search

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:06:34.033944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.