Paper Citation Record · LEDGER

High Accuracy and High Fidelity Extraction of Neural Networks

As of 15 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:1909.01838.

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

pith.paper-citation-record.v1
1909.01838 v2

Coverage vector

measured 62 of 62 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:53:15.852699Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation cd3cddbe-7c07-43f7-b992-2f970676329e · outbound

This paper cites Energy and Policy Considerations for Deep Learning in NLP.

High Accuracy and High Fidelity Extraction of Neural Networks Energy and Policy Considerations for Deep Learning in NLP

Reference 1

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This paper cites Xlnet: Generalized autoregressive pretraining for language understanding,.

High Accuracy and High Fidelity Extraction of Neural Networks Xlnet: Generalized autoregressive pretraining for language understanding,

Reference 2

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This paper cites The unreasonable effectiveness of data,.

High Accuracy and High Fidelity Extraction of Neural Networks The unreasonable effectiveness of data,

Reference 3

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This paper cites Imagenet: A large-scale hierarchical image database,.

High Accuracy and High Fidelity Extraction of Neural Networks Imagenet: A large-scale hierarchical image database,

Reference 4

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This paper cites Sequence to sequence learning with neural networks,.

High Accuracy and High Fidelity Extraction of Neural Networks Sequence to sequence learning with neural networks,

Reference 5

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This paper cites Wavenet: A generative model for raw audio.

High Accuracy and High Fidelity Extraction of Neural Networks Wavenet: A generative model for raw audio

Reference 6

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This paper cites Practical black-box attacks against machine learning,.

High Accuracy and High Fidelity Extraction of Neural Networks Practical black-box attacks against machine learning,

Reference 7

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This paper cites Adversarial learning,.

High Accuracy and High Fidelity Extraction of Neural Networks Adversarial learning,

Reference 8

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This paper cites Membership inference attacks against machine learn- ing models,.

High Accuracy and High Fidelity Extraction of Neural Networks Membership inference attacks against machine learn- ing models,

Reference 9

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This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

High Accuracy and High Fidelity Extraction of Neural Networks ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 10

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This paper cites Stealing machine learning models via pre- diction apis,.

High Accuracy and High Fidelity Extraction of Neural Networks Stealing machine learning models via pre- diction apis,

Reference 11

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This paper cites Knockoff nets: Stealing functionality of black-box models,.

High Accuracy and High Fidelity Extraction of Neural Networks Knockoff nets: Stealing functionality of black-box models,

Reference 12

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This paper cites Exploring Connections Between Active Learning and Model Extraction.

High Accuracy and High Fidelity Extraction of Neural Networks Exploring Connections Between Active Learning and Model Extraction

Reference 13

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This paper cites Towards Reverse-Engineering Black-Box Neural Networks.

High Accuracy and High Fidelity Extraction of Neural Networks Towards Reverse-Engineering Black-Box Neural Networks

Reference 14

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This paper cites A framework for the extraction of Deep Neural Networks by leveraging public data.

High Accuracy and High Fidelity Extraction of Neural Networks A framework for the extraction of Deep Neural Networks by leveraging public data

Reference 15

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This paper cites Copycat cnn: Steal- ing knowledge by persuading confession with random non-labeled data,.

High Accuracy and High Fidelity Extraction of Neural Networks Copycat cnn: Steal- ing knowledge by persuading confession with random non-labeled data,

Reference 16

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Observation 9b31a90f-2c63-4780-85eb-da684a40b181 · outbound

This paper cites Overlearning Reveals Sensitive Attributes.

High Accuracy and High Fidelity Extraction of Neural Networks Overlearning Reveals Sensitive Attributes

Reference 17

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This paper cites Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks.

High Accuracy and High Fidelity Extraction of Neural Networks Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks

Reference 18

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This paper cites Model Reconstruction from Model Explanations.

High Accuracy and High Fidelity Extraction of Neural Networks Model Reconstruction from Model Explanations

Reference 19

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This paper cites Rectiﬁed linear units im- prove restricted boltzmann machines,.

High Accuracy and High Fidelity Extraction of Neural Networks Rectiﬁed linear units im- prove restricted boltzmann machines,

Reference 20

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This paper cites A method for solving the convex pro- gramming problem with convergence rate o (1/kˆ 2),.

High Accuracy and High Fidelity Extraction of Neural Networks A method for solving the convex pro- gramming problem with convergence rate o (1/kˆ 2),

Reference 21

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High Accuracy and High Fidelity Extraction of Neural Networks Adaptive subgradient methods for online learning and stochastic optimization,

Reference 22

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This paper cites Adam: A Method for Stochastic Optimization.

High Accuracy and High Fidelity Extraction of Neural Networks Adam: A Method for Stochastic Optimization

Reference 23

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High Accuracy and High Fidelity Extraction of Neural Networks Distilling the Knowledge in a Neural Network

Reference 24

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This paper cites CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information.

High Accuracy and High Fidelity Extraction of Neural Networks CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Reference 25

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High Accuracy and High Fidelity Extraction of Neural Networks Differential power anal- ysis,

Reference 26

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High Accuracy and High Fidelity Extraction of Neural Networks On the Learnability of Deep Random Networks

Reference 27

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High Accuracy and High Fidelity Extraction of Neural Networks Exploring the limits of weakly supervised pretraining,

Reference 28

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High Accuracy and High Fidelity Extraction of Neural Networks Zero-shot Knowledge Transfer via Adversarial Belief Matching

Reference 29

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High Accuracy and High Fidelity Extraction of Neural Networks Language models are unsupervised multi- task learners,

Reference 30

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High Accuracy and High Fidelity Extraction of Neural Networks Cnn features off-the-shelf: an astounding baseline for recognition,

Reference 31

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High Accuracy and High Fidelity Extraction of Neural Networks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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High Accuracy and High Fidelity Extraction of Neural Networks Queries and concept learning,

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High Accuracy and High Fidelity Extraction of Neural Networks Combining labeled and un- labeled data with co-training,

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High Accuracy and High Fidelity Extraction of Neural Networks Com- bining mixmatch and active learning for better accuracy with fewer labels,

Reference 35

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High Accuracy and High Fidelity Extraction of Neural Networks Rethinking deep active learning: Using unlabeled data at model training,

Reference 36

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High Accuracy and High Fidelity Extraction of Neural Networks S4L: Self-Supervised Semi-Supervised Learning

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High Accuracy and High Fidelity Extraction of Neural Networks MixMatch: A Holistic Approach to Semi-Supervised Learning

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High Accuracy and High Fidelity Extraction of Neural Networks Reading digits in natural images with unsu- pervised feature learning,

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High Accuracy and High Fidelity Extraction of Neural Networks Learning multiple layers of fea- tures from tiny images,

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High Accuracy and High Fidelity Extraction of Neural Networks Hidden technical debt in machine learn- ing systems,

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High Accuracy and High Fidelity Extraction of Neural Networks Sim- ple and scalable predictive uncertainty estimation using deep ensembles,

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High Accuracy and High Fidelity Extraction of Neural Networks Unresolved cited work

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High Accuracy and High Fidelity Extraction of Neural Networks Prototypical examples in deep learning: Metrics, characteristics, and utility,

Reference 44

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High Accuracy and High Fidelity Extraction of Neural Networks Gradient-based learning applied to document recog- nition,

Reference 45

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High Accuracy and High Fidelity Extraction of Neural Networks Unresolved cited work

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High Accuracy and High Fidelity Extraction of Neural Networks Intriguing properties of neural networks

Reference 47

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High Accuracy and High Fidelity Extraction of Neural Networks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 48

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High Accuracy and High Fidelity Extraction of Neural Networks Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations

Reference 49

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High Accuracy and High Fidelity Extraction of Neural Networks Adding robustness to support vector machines against adver- sarial reverse engineering,

Reference 50

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High Accuracy and High Fidelity Extraction of Neural Networks PRADA: Protecting against DNN Model Stealing Attacks

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High Accuracy and High Fidelity Extraction of Neural Networks Model extraction warning in mlaas paradigm,

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High Accuracy and High Fidelity Extraction of Neural Networks Stealing hyperparameters in machine learning,

Reference 53

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High Accuracy and High Fidelity Extraction of Neural Networks Protecting intellectual prop- erty of deep neural networks with watermarking,

Reference 54

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b02bf6b-ee88-4d64-ba4a-19f6f5ff3386 · outbound

This paper cites Em- bedding watermarks into deep neural networks,.

High Accuracy and High Fidelity Extraction of Neural Networks Em- bedding watermarks into deep neural networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.689590Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 161c1aa8-af42-4c26-a55b-e58c52555b4b · outbound

This paper cites On the (im) possi- bility of obfuscating programs,.

High Accuracy and High Fidelity Extraction of Neural Networks On the (im) possi- bility of obfuscating programs,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.674083Z

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Observation 5511c474-018f-4bc3-a5bd-76e12f484cc3 · outbound

This paper cites A privacy-preserving protocol for neural-network-based computation,.

High Accuracy and High Fidelity Extraction of Neural Networks A privacy-preserving protocol for neural-network-based computation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.658573Z

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Observation e52caf90-cd24-4cf4-af8f-eeb4905ff303 · outbound

This paper cites Reluplex: An efﬁcient smt solver for verifying deep neural networks,.

High Accuracy and High Fidelity Extraction of Neural Networks Reluplex: An efﬁcient smt solver for verifying deep neural networks,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.642509Z

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Observation 77b3378f-f0b5-4d62-9fbe-96fb9570e377 · outbound

This paper cites This step is the most nontrivial to analyze, but fortunately this was addressed in [19].

High Accuracy and High Fidelity Extraction of Neural Networks This step is the most nontrivial to analyze, but fortunately this was addressed in [19]

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.627517Z

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Observation 2e7a98df-8a71-42b7-a3f4-989136ac58c4 · outbound

This paper cites This piece is signiﬁcantly compli- cated by not having access to gradient queries.

High Accuracy and High Fidelity Extraction of Neural Networks This piece is signiﬁcantly compli- cated by not having access to gradient queries

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.612038Z

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Observation 261e1bf4-535c-4800-8bbc-9ccefe0abd9c · outbound

This paper cites For each ReLU, we require only three queries.

High Accuracy and High Fidelity Extraction of Neural Networks For each ReLU, we require only three queries

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.596067Z

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Observation c1dcc877-778e-4e71-a32c-04ccab3aae80 · outbound

This paper cites This step requires h queries to make the system of linear equations full rank (although in practice we reuse previous queries here, making this step require 0 queries).

High Accuracy and High Fidelity Extraction of Neural Networks This step requires h queries to make the system of linear equations full rank (although in practice we reuse previous queries here, making this step require 0 queries)

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:27:50.580757Z

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Pith citing papers

Observation 10d43d7d-8002-4fdc-9539-9ddd9a49c361 · inbound

Bounded Behavioral Indistinguishability for Black-Box LLM Distillation cites this paper.

Bounded Behavioral Indistinguishability for Black-Box LLM Distillation High Accuracy and High Fidelity Extraction of Neural Networks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:53:15.854345Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.