Paper Citation Record · LEDGER

What Do Compressed Deep Neural Networks Forget?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:1911.05248.

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

pith.paper-citation-record.v1
1911.05248 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:41:07.648596Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:09:38.117469Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6c354658-3a57-4c0a-abb3-defdd49047f8 · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation What Do Compressed Deep Neural Networks Forget?

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:56:23.495209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c28810d-e176-448f-866a-0f031a981a10 · inbound

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications cites this paper.

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications What Do Compressed Deep Neural Networks Forget?

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:08:41.918296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b3572c33-a571-4546-aa11-107e1c90fd6d · inbound

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification cites this paper.

SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification What Do Compressed Deep Neural Networks Forget?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:41:07.648596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 771deed6-1eea-4371-abac-05632ab067b9 · inbound

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis cites this paper.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis What Do Compressed Deep Neural Networks Forget?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:01.096012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d9d0386-5dd7-48a9-a5b8-9cd7ba88c11f · inbound

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning cites this paper.

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning What Do Compressed Deep Neural Networks Forget?

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-05T22:09:04.257495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abfd3e7b-a141-4625-b034-9abe1f94a506 · inbound

Compressed Models are NOT Trust-equivalent to Their Large Counterparts cites this paper.

Compressed Models are NOT Trust-equivalent to Their Large Counterparts What Do Compressed Deep Neural Networks Forget?

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T19:00:30.057802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 276e11e4-bdf9-451d-a32e-1208416452e2 · inbound

The Uneven Impact of Post-Training Quantization in Machine Translation cites this paper.

The Uneven Impact of Post-Training Quantization in Machine Translation What Do Compressed Deep Neural Networks Forget?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:26.502581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96659789-0933-43bf-b38f-47269ae135d2 · inbound

Explaining How Quantization Disparately Skews a Model cites this paper.

Explaining How Quantization Disparately Skews a Model What Do Compressed Deep Neural Networks Forget?

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T22:41:17.308780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5de1d058-fa26-4ca9-bf49-daef29b63585 · inbound

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models cites this paper.

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models What Do Compressed Deep Neural Networks Forget?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:16:15.094198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6731ec8-7aa3-45a3-a1cc-d275ef3e5fb3 · inbound

Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI cites this paper.

Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI What Do Compressed Deep Neural Networks Forget?

Reference 272

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:50:28.356500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 36f2077d-1083-4a6e-ae24-ae8f012aad28 · inbound

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI cites this paper.

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI What Do Compressed Deep Neural Networks Forget?

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.635904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 98905bc3-8204-4352-937a-2a3a3036ddc9 · inbound

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels cites this paper.

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels What Do Compressed Deep Neural Networks Forget?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:57:42.535481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7ef2040-aca6-4d23-b088-915aa3f43b39 · inbound

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters cites this paper.

Sigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters What Do Compressed Deep Neural Networks Forget?

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:26.594718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 125b98df-95f0-4277-ac2f-33c276d842b5 · inbound

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study cites this paper.

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study What Do Compressed Deep Neural Networks Forget?

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:03.524534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6836beab-1e7e-4fce-803b-62c216bcecd1 · inbound

DPIFrame: A Dual-Level Parallelism Acceleration Framework for CTR Model Inference cites this paper.

DPIFrame: A Dual-Level Parallelism Acceleration Framework for CTR Model Inference What Do Compressed Deep Neural Networks Forget?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:09:38.118891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0bdf643e-8dcb-4836-9a6a-5ff7d93902fb · inbound

When Token Compression Breaks: Structural Pruning vs. Token Reduction for Robust ViT Segmentation under High Compression cites this paper.

When Token Compression Breaks: Structural Pruning vs. Token Reduction for Robust ViT Segmentation under High Compression What Do Compressed Deep Neural Networks Forget?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:58:37.550540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7e2a7743-27ca-4730-8b53-e73a8f7daf1c · inbound

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs cites this paper.

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs What Do Compressed Deep Neural Networks Forget?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:54.456189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.