Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-24T16:21:56.723289Z
Paper Citation Record · LEDGER
DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
As of 6 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:1907.11065.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
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, observed 2026-05-10T05:38:12.882914Z
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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
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 2026-08-05T06:32:48.257954+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 2026-08-05T06:32:48.257954+00:00.