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Paper Citation Record · LEDGER

Adversarial Robustness through Dynamic Ensemble Learning

As of 12 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.16254.

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

pith.paper-citation-record.v1
2412.16254 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:22:20.208006Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved4
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6e3a1d99-b417-4a24-880e-e047d12d1f55 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Adversarial Robustness through Dynamic Ensemble Learning Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 95c516b7-8f21-466c-9ffe-86152481a7e1 · outbound

This paper cites Word -level textual adversarial attack method based on differential evolution algorithm,.

Adversarial Robustness through Dynamic Ensemble Learning Word -level textual adversarial attack method based on differential evolution algorithm,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation e630a51f-ccf8-4fab-b2fa-5837fb60f7ad · outbound

This paper cites A modified word saliency-based adversarial text attack,.

Adversarial Robustness through Dynamic Ensemble Learning A modified word saliency-based adversarial text attack,

Reference 3

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Source-reported events for the cited work

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Observation b9cf6ff6-72f8-44e4-940b-7c4122ebfdb2 · outbound

This paper cites Saliency attention and semantic similarity-driven adversarial perturbation,.

Adversarial Robustness through Dynamic Ensemble Learning Saliency attention and semantic similarity-driven adversarial perturbation,

Reference 4

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 93922730-0d17-49cc-bde0-ba0e0b515c17 · outbound

This paper cites Revisiting Character-level Adversarial Attacks for Language Models.

Adversarial Robustness through Dynamic Ensemble Learning Revisiting Character-level Adversarial Attacks for Language Models

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 177d4af8-05f1-4053-81da-2b7fbee3cbb2 · outbound

This paper cites Character-level white -box adversarial attacks against transformers via attachable subwords substitution,.

Adversarial Robustness through Dynamic Ensemble Learning Character-level white -box adversarial attacks against transformers via attachable subwords substitution,

Reference 6

Resolution
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raw_fallback, observed 2026-08-11T11:22:20.790574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 00055ddb-cbd6-41ae-97d6-eede3c283899 · outbound

This paper cites Defense against adversarial attacks via textual embeddings based on semantic associative field,.

Adversarial Robustness through Dynamic Ensemble Learning Defense against adversarial attacks via textual embeddings based on semantic associative field,

Reference 7

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raw_fallback, observed 2026-08-11T11:22:20.764977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 235d9f90-eb17-4661-a6da-42983d007c4b · outbound

This paper cites Phrase-level textual adversarial attack with label preservationn,.

Adversarial Robustness through Dynamic Ensemble Learning Phrase-level textual adversarial attack with label preservationn,

Reference 8

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raw_fallback, observed 2026-08-11T11:22:20.743165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c15beb32-aa4f-4023-bf82-0252cdca68aa · outbound

This paper cites Explaining and harnessing adversarial examples.

Adversarial Robustness through Dynamic Ensemble Learning Explaining and harnessing adversarial examples

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation fed22d08-6f10-4626-84c2-fcdc55cb27c0 · outbound

This paper cites Estimating raining data influence by tracing gradient descent.

Adversarial Robustness through Dynamic Ensemble Learning Estimating raining data influence by tracing gradient descent

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0b02d5bd-00a6-4471-8261-fe60cca3bd7c · outbound

This paper cites Interpretable adversarial perturbation in input embedding space for text.

Adversarial Robustness through Dynamic Ensemble Learning Interpretable adversarial perturbation in input embedding space for text

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 08763c27-c44a-421d-ac98-ff03f643da37 · outbound

This paper cites Adversarial examples for evaluating reading comprehension systems.

Adversarial Robustness through Dynamic Ensemble Learning Adversarial examples for evaluating reading comprehension systems

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bbee5714-8086-4709-9a52-7e4626bb7605 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Adversarial Robustness through Dynamic Ensemble Learning Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:22:20.625715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation eb494b67-74dc-4a10-8e1f-1d3a8b635a67 · outbound

This paper cites DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising.

Adversarial Robustness through Dynamic Ensemble Learning DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation dff1dd2d-834d-47f1-ac36-5dec60739d1b · outbound

This paper cites Randomized smoothing with masked inference for adversarially robust text classifications,.

Adversarial Robustness through Dynamic Ensemble Learning Randomized smoothing with masked inference for adversarially robust text classifications,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation cefb25ae-64cf-4ad9-9408-033d3a5e7429 · outbound

This paper cites Is bert really robust? A strong baseline for natural language attack on text classification and entailment,.

Adversarial Robustness through Dynamic Ensemble Learning Is bert really robust? A strong baseline for natural language attack on text classification and entailment,

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-11T11:22:20.563261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 368a9c64-33f2-4d58-8614-0b11bb8f63b8 · outbound

This paper cites TextBugger: Generating adversarial text against real -world applications,.

Adversarial Robustness through Dynamic Ensemble Learning TextBugger: Generating adversarial text against real -world applications,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f4c48e81-97fd-4bbd-a636-ce4c312f4ada · outbound

This paper cites BERT-Attack: Adversarial attack against BERT using BERT,.

Adversarial Robustness through Dynamic Ensemble Learning BERT-Attack: Adversarial attack against BERT using BERT,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 665e10be-bbea-46cd-aa03-90aff171e08f · outbound

This paper cites Character-level Convolutional Networks for Text Classification,.

Adversarial Robustness through Dynamic Ensemble Learning Character-level Convolutional Networks for Text Classification,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation e6e82ea9-17b2-4bc5-b246-dc681be49df1 · outbound

This paper cites Learning word vectors for sentiment analysis,.

Adversarial Robustness through Dynamic Ensemble Learning Learning word vectors for sentiment analysis,

Reference 20

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0dd8e30a-8aa1-4939-99a1-25b12fcea3b8 · outbound

This paper cites GLUE: A multi -task benchmark and analysis platform for natural language understanding,.

Adversarial Robustness through Dynamic Ensemble Learning GLUE: A multi -task benchmark and analysis platform for natural language understanding,

Reference 21

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raw_fallback, observed 2026-08-11T11:22:20.448934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0490d4ab-f69c-4c98-a066-ff5df9b9c860 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference,.

Adversarial Robustness through Dynamic Ensemble Learning A broad-coverage challenge corpus for sentence understanding through inference,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation ddc5eebc-42bb-4271-a720-49ca46cd0feb · outbound

This paper cites BERT: Pre - training of deep bi -directional transformers for language understanding,.

Adversarial Robustness through Dynamic Ensemble Learning BERT: Pre - training of deep bi -directional transformers for language understanding,

Reference 23

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raw_fallback, observed 2026-08-11T11:22:20.404040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation a4196b7a-5c54-442b-844c-ae7fc2fa2cb6 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Adversarial Robustness through Dynamic Ensemble Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 24

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no resolver link, observed 2026-08-11T11:22:20.182235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:22:20.182235Z digest=sha256:d21251b59b3a456ee42cc64abf0d50e1dc7312f8e863e387bc6c22020a03cf46

Observation 4c416917-680f-4c4f-8c72-36374807dd9e · outbound

This paper cites ALBERT: A lite BERT for self -supervised learning of language representations,.

Adversarial Robustness through Dynamic Ensemble Learning ALBERT: A lite BERT for self -supervised learning of language representations,

Reference 25

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raw_fallback, observed 2026-08-11T11:22:20.384027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b8d44a70-3d74-406f-8e0b-a22a2e4a0853 · outbound

This paper cites InfoBERT: Improving robustness of lang uage models from an information theoretic perspective,.

Adversarial Robustness through Dynamic Ensemble Learning InfoBERT: Improving robustness of lang uage models from an information theoretic perspective,

Reference 26

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raw_fallback, observed 2026-08-11T11:22:20.365944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 1339ee51-a8c3-406f-a1cb-89ec2ca4bbad · outbound

This paper cites Searching for an effective defender: Benchmarking defense against adversarial word substitution,.

Adversarial Robustness through Dynamic Ensemble Learning Searching for an effective defender: Benchmarking defense against adversarial word substitution,

Reference 27

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raw_fallback, observed 2026-08-11T11:22:20.347969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:22:20.200422Z digest=sha256:664f836089b633a3e209541cc86d14ebfaf04a17792cfcf0eb0c736b253dd0e2

Observation c5db83b5-bd35-4cb9-b4b7-693fe555ddc7 · outbound

This paper cites FreeLB: Enhanced Adversarial Training for Natural Language Understanding.

Adversarial Robustness through Dynamic Ensemble Learning FreeLB: Enhanced Adversarial Training for Natural Language Understanding

Reference 28

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unresolved
no resolver link, observed 2026-08-11T11:22:20.208006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:22:20.208006Z digest=sha256:29c451512445af5da048cdf193aacccec83a273caf29e9546832496e45893ff5

Pith citing papers

No inbound Pith citation observations are available.