Paper Citation Record · LEDGER

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

As of 22 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 4 inbound Pith citation observations for arXiv:1907.11065.

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

pith.paper-citation-record.v1
1907.11065 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T16:21:56.723289Z

measured 20 of 20 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:46:43.573794Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

16 of 16 outbound references displayed

verified exact13
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

34
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 455e1d63-0152-4ba9-b2ff-e52b0495b38d · outbound

This paper cites Layer Normalization.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Layer Normalization

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.604044Z

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 cfee279b-89c1-47f5-8daa-2e7b356b3ba9 · outbound

This paper cites A large annotated corpus for learning natural language inference.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks A large annotated corpus for learning natural language inference

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T16:24:40.597594Z

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 3aa9fcc1-20d4-4ec8-a5a2-3fd871e56814 · outbound

This paper cites A Fast Unified Model for Parsing and Sentence Understanding.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks A Fast Unified Model for Parsing and Sentence Understanding

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.665328Z

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 03b9a047-c677-4f9c-8ab0-1fc840b3ab11 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Improved Regularization of Convolutional Neural Networks with Cutout

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.629341Z

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 2b98b9c5-37c2-4eff-8e92-3f8d6f04dbf2 · outbound

This paper cites Shake-Shake regularization.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Shake-Shake regularization

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.660558Z

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 99add77e-1a44-4f6b-b734-005b95635f94 · outbound

This paper cites Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.654846Z

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 be3f2a85-1d4d-4009-a534-38daabf7007a · outbound

This paper cites FractalNet: Ultra-Deep Neural Networks without Residuals.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks FractalNet: Ultra-Deep Neural Networks without Residuals

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.619522Z

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 029888f5-3fce-4f4a-ac9e-a12fe88822bd · outbound

This paper cites Multi-Head Attention with Disagreement Regularization.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Multi-Head Attention with Disagreement Regularization

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.650077Z

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 76e96180-c9bd-4dfe-a0ab-bc64bed8da73 · outbound

This paper cites A Structured Self-attentive Sentence Embedding.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks A Structured Self-attentive Sentence Embedding

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.639360Z

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 765f4032-333a-454f-832f-9fa8a1cff8cc · outbound

This paper cites Scaling Neural Machine Translation.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Scaling Neural Machine Translation

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.614014Z

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 bf86cfd3-6cc2-4959-b976-e580facc9aa4 · outbound

This paper cites Recurrent Dropout without Memory Loss.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Recurrent Dropout without Memory Loss

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.608947Z

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 717f01bd-1150-44bc-b59f-4b45acca9ea4 · outbound

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

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Neural Machine Translation of Rare Words with Subword Units

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.624403Z

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 a946d029-c64d-49ad-8e59-0df37f61023c · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Dropout: a simple way to prevent neural networks from overﬁtting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:24:41.723911Z

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 2bf2915d-f25e-4ba3-8513-2b03f9650ac6 · outbound

This paper cites Document modeling with gated recurrent neural network for sentiment classiﬁcation.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Document modeling with gated recurrent neural network for sentiment classiﬁcation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:24:41.720276Z

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 fb610ff7-6f68-4d04-b92f-089923217bb5 · outbound

This paper cites ShakeDrop Regularization for Deep Residual Learning.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks ShakeDrop Regularization for Deep Residual Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-24T16:24:40.644605Z

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 1612a02a-b768-4bb1-a41c-904e4749f701 · outbound

This paper cites Multi-Task Cross-Lingual Sequence Tagging from Scratch.

DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks Multi-Task Cross-Lingual Sequence Tagging from Scratch

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:24:40.633858Z

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.

Pith citing papers

Observation 7752d722-275c-47e2-a23a-c363ea5c1b53 · inbound

Reasoning Bias of Next Token Prediction Training cites this paper.

Reasoning Bias of Next Token Prediction Training DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T13:46:43.573794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f3d46d3-115d-4d92-95bf-891b87324480 · inbound

Self-Supervised Learning for Solar Radio Spectrum Classification cites this paper.

Self-Supervised Learning for Solar Radio Spectrum Classification DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:00.696842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47a04ff2-2b44-42cb-99dc-edf3e28a25f5 · inbound

Language models recognize dropout and Gaussian noise applied to their activations cites this paper.

Language models recognize dropout and Gaussian noise applied to their activations DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T23:49:42.666573Z

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 7aab1a54-bdef-4a3d-b301-ed8851fad4e4 · inbound

Explicit Dropout: Deterministic Regularization for Transformer Architectures cites this paper.

Explicit Dropout: Deterministic Regularization for Transformer Architectures DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T23:49:42.666573Z

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.