Paper Citation Record · LEDGER

Differentiable Product Quantization for End-to-End Embedding Compression

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:1908.09756.

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

pith.paper-citation-record.v1
1908.09756 v3

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:08:34.765272Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy4
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54ea8013-e559-49b2-849d-13499a9919dd · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Differentiable Product Quantization for End-to-End Embedding Compression Categorical Reparameterization with Gumbel-Softmax

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.193331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf6c516b-fe88-4dac-9ac3-0efd9550c977 · outbound

This paper cites Fast Decoding in Sequence Models using Discrete Latent Variables.

Differentiable Product Quantization for End-to-End Embedding Compression Fast Decoding in Sequence Models using Discrete Latent Variables

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.207485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0eb7d56-e806-4828-a461-5530003ad612 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Differentiable Product Quantization for End-to-End Embedding Compression Efficient Estimation of Word Representations in Vector Space

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.503579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 220327cc-8abb-49df-9ae0-08d5bc59fc54 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Differentiable Product Quantization for End-to-End Embedding Compression Neural Machine Translation of Rare Words with Subword Units

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.515088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed807279-edf4-4917-b42a-0936a7df2c85 · outbound

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

Differentiable Product Quantization for End-to-End Embedding Compression Compressing Word Embeddings via Deep Compositional Code Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.566133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97918a2c-1d5a-4f18-b36f-2e3465992d3c · outbound

This paper cites Recurrent Neural Network Regularization.

Differentiable Product Quantization for End-to-End Embedding Compression Recurrent Neural Network Regularization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.710694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4853b54-5c69-415f-820d-4051927823e4 · outbound

This paper cites Proof of Proposition 1 Proof.

Differentiable Product Quantization for End-to-End Embedding Compression Proof of Proposition 1 Proof

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:36.109059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dbe79b0b-acba-4453-adea-b2f0f37b89a2 · outbound

This paper cites All models were trained with a batch size of 2048 sentences for 250k steps, and with the SM3 optimizer (Anil et al.,.

Differentiable Product Quantization for End-to-End Embedding Compression All models were trained with a batch size of 2048 sentences for 250k steps, and with the SM3 optimizer (Anil et al.,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:36.005981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ed559cf-d109-4d12-a6db-0c6f8af0fbf8 · outbound

This paper cites For the DPQ experiments, we used DPQ-SX with no subspace-sharing, D = 128 andK = 32, and exactly the same conﬁgurations and hyperparameters as in our baseline.

Differentiable Product Quantization for End-to-End Embedding Compression For the DPQ experiments, we used DPQ-SX with no subspace-sharing, D = 128 andK = 32, and exactly the same conﬁgurations and hyperparameters as in our baseline

Reference 1024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:35.907523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8001d799-e38c-4336-b4f0-8834deb1f2ed · outbound

This paper cites and Richardson, J.

Differentiable Product Quantization for End-to-End Embedding Compression and Richardson, J

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:08:36.185858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5dde2c2-1d9f-49d6-8cb2-2d88ffc31335 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Differentiable Product Quantization for End-to-End Embedding Compression BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:33.893625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da5a8060-ad3c-49df-a670-5a8975f8a237 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Differentiable Product Quantization for End-to-End Embedding Compression MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:33.941084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf501902-2316-4d36-b471-3e65c4bdb1fa · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Differentiable Product Quantization for End-to-End Embedding Compression Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:34.051090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a6e2705-c439-4de7-a604-11991bfbd59f · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Differentiable Product Quantization for End-to-End Embedding Compression Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:33.927497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db9ec727-b479-4ab3-b132-019c68e2accd · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Differentiable Product Quantization for End-to-End Embedding Compression Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-14T11:08:33.848309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.