ADS-C: Antidistillation Sampling for Classification | Paper Citation Record

Observation 30c13e43-6395-4411-8494-f3ef72853581 · outbound

This paper cites Learning complex, extended sequences using the principle of history compression,.

ADS-C: Antidistillation Sampling for Classification Learning complex, extended sequences using the principle of history compression,

Reference 1

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Observation 022411af-9cab-436d-b182-49f5e3f2ca95 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ADS-C: Antidistillation Sampling for Classification Distilling the Knowledge in a Neural Network

Reference 2

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Observation 121e4a97-1c99-42b5-93a3-b6e02df938c2 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

ADS-C: Antidistillation Sampling for Classification MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 3

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Observation 39f1de27-ef7c-4173-a5a0-27f0e73c844e · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

ADS-C: Antidistillation Sampling for Classification DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 4

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Observation 9900d72b-d848-442c-a48f-72afce4a3fd9 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

ADS-C: Antidistillation Sampling for Classification Training data-efficient image transformers & distillation through attention,

Reference 5

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Observation 40d1e6d1-01a2-4a58-b2ce-0a4a2ba7ded0 · outbound

This paper cites Knowledge distillation: A survey,.

ADS-C: Antidistillation Sampling for Classification Knowledge distillation: A survey,

Reference 6

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Observation 64470911-9015-408b-8763-8a0b2bbd91bd · outbound

This paper cites Stealing machine learning models via prediction{APIs},.

ADS-C: Antidistillation Sampling for Classification Stealing machine learning models via prediction{APIs},

Reference 7

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Observation 2ce9659f-f90a-4ab5-9239-6302c04f5dcc · outbound

This paper cites A survey on model extraction attacks and defenses for large language models,.

ADS-C: Antidistillation Sampling for Classification A survey on model extraction attacks and defenses for large language models,

Reference 8

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Observation c31efe10-d0c2-426d-984d-272364c728c9 · outbound

This paper cites DeepSeek trained AI model using distillation, now a dis- ruptive force,.

ADS-C: Antidistillation Sampling for Classification DeepSeek trained AI model using distillation, now a dis- ruptive force,

Reference 9

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Observation 3ae36f06-2fe5-47cc-ac3d-e22199e396ed · outbound

This paper cites Detecting and preventing distillation attacks,.

ADS-C: Antidistillation Sampling for Classification Detecting and preventing distillation attacks,

Reference 10

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Observation e209b7e1-bcc3-441e-a1e5-24811f375a8f · outbound

This paper cites Deep Intellectual Property Protection: A Survey.

ADS-C: Antidistillation Sampling for Classification Deep Intellectual Property Protection: A Survey

Reference 11

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Observation 33e9605a-17da-430b-aee6-46193756718a · outbound

This paper cites Intellectual property protection for deep learning model and dataset intelligence,.

ADS-C: Antidistillation Sampling for Classification Intellectual property protection for deep learning model and dataset intelligence,

Reference 12

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Observation 0a02e48e-6904-48e8-ab5e-d0883030aa49 · outbound

This paper cites Membership inference attacks against machine learning models,.

ADS-C: Antidistillation Sampling for Classification Membership inference attacks against machine learning models,

Reference 13

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Observation 2f990f9f-6242-4cc5-b7bd-1b0790782f9c · outbound

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

ADS-C: Antidistillation Sampling for Classification ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 14

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Observation 2b54a3f9-226b-4772-bb19-a52787462cfc · outbound

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

ADS-C: Antidistillation Sampling for Classification Practical black-box attacks against machine learning,

Reference 15

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Observation ebc8e058-bdee-4ea2-ae6d-8c86ade158a8 · outbound

This paper cites Evasion attacks against machine learning at test time,.

ADS-C: Antidistillation Sampling for Classification Evasion attacks against machine learning at test time,

Reference 16

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Observation 424b66ac-0242-4f9f-b298-ed46b5f15770 · outbound

This paper cites Adversarial learning,.

ADS-C: Antidistillation Sampling for Classification Adversarial learning,

Reference 17

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Observation 783a80a5-acb3-451f-804a-175ce3f2cae1 · outbound

This paper cites Privacy in pharmacogenetics: An{End-to-End}case study of person- alized warfarin dosing,.

ADS-C: Antidistillation Sampling for Classification Privacy in pharmacogenetics: An{End-to-End}case study of person- alized warfarin dosing,

Reference 18

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Observation 75d44baf-be61-49b3-9951-5a62673cf0df · outbound

This paper cites Model inversion attacks that exploit confidence information and basic countermeasures,.

ADS-C: Antidistillation Sampling for Classification Model inversion attacks that exploit confidence information and basic countermeasures,

Reference 19

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Observation 3e11b25b-a309-45e2-80d4-56db5bce3972 · outbound

This paper cites Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,.

ADS-C: Antidistillation Sampling for Classification Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,

Reference 20

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Observation 9b727c59-e84a-475b-8ebf-bc587c02abf8 · outbound

This paper cites Artificial intelligence and machine learn- ing in clinical medicine, 2023,.

ADS-C: Antidistillation Sampling for Classification Artificial intelligence and machine learn- ing in clinical medicine, 2023,

Reference 21

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Observation aec2fe46-760f-4fc8-bd01-c845d786dea1 · outbound

This paper cites Financial fraud detection through the application of machine learning techniques: a literature review,.

ADS-C: Antidistillation Sampling for Classification Financial fraud detection through the application of machine learning techniques: a literature review,

Reference 22

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Observation aa414ec0-1eeb-4a9f-b592-76105a8829fb · outbound

This paper cites Learn- ing lightweight object detectors via multi-teacher progressive distilla- tion,.

ADS-C: Antidistillation Sampling for Classification Learn- ing lightweight object detectors via multi-teacher progressive distilla- tion,

Reference 23

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Observation ec44827c-915e-427a-a549-7ebbb6b79287 · outbound

This paper cites Antidistillation sampling,.

ADS-C: Antidistillation Sampling for Classification Antidistillation sampling,

Reference 24

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Observation b521e6b0-85d4-4668-b93d-c887b48f109a · outbound

This paper cites Defending against neural network model stealing attacks using deceptive perturbations,.

ADS-C: Antidistillation Sampling for Classification Defending against neural network model stealing attacks using deceptive perturbations,

Reference 25

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Observation 468f5835-5c16-4177-849a-cff0841fe820 · outbound

This paper cites D-dae: Defense- penetrating model extraction attacks,.

ADS-C: Antidistillation Sampling for Classification D-dae: Defense- penetrating model extraction attacks,

Reference 26

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Observation 7b5a7eb8-6bd0-4988-aef5-9055bc7ce3a5 · outbound

This paper cites On calibration of modern neural networks,.

ADS-C: Antidistillation Sampling for Classification On calibration of modern neural networks,

Reference 27

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Observation 8e7e4002-a315-4c2f-b94a-2f6d75b09816 · outbound

This paper cites Model compres- sion,.

ADS-C: Antidistillation Sampling for Classification Model compres- sion,

Reference 28

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Observation 4b622548-0c5a-4a97-8884-2f52c96d65c7 · outbound

This paper cites Do deep nets really need to be deep?.

ADS-C: Antidistillation Sampling for Classification Do deep nets really need to be deep?

Reference 29

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Observation 3017901f-abc9-467f-9d76-a72275f0e310 · outbound

This paper cites Born again neural networks,.

ADS-C: Antidistillation Sampling for Classification Born again neural networks,

Reference 30

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Observation ada97991-d776-4feb-bb34-3f307bdde3db · outbound

This paper cites Understanding and Improving Knowledge Distillation.

ADS-C: Antidistillation Sampling for Classification Understanding and Improving Knowledge Distillation

Reference 31

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Observation 0c0112db-ccd3-4751-a2ca-86ce676669fd · outbound

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

ADS-C: Antidistillation Sampling for Classification Knockoff nets: Stealing functionality of black-box models,

Reference 32

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Observation 0f71540d-ee4b-40a0-b278-ede6942eca09 · outbound

This paper cites Data-free model extraction,.

ADS-C: Antidistillation Sampling for Classification Data-free model extraction,

Reference 33

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Observation 9a063281-9845-4c4e-b993-70d67cdf617b · outbound

This paper cites Black-box behavioral distillation breaks safety alignment in medical llms,.

ADS-C: Antidistillation Sampling for Classification Black-box behavioral distillation breaks safety alignment in medical llms,

Reference 34

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Observation 653030e9-c9fa-4c31-9e88-1ec1ae7cd72a · outbound

This paper cites I know what you trained last summer: A survey on stealing machine learning models and defences,.

ADS-C: Antidistillation Sampling for Classification I know what you trained last summer: A survey on stealing machine learning models and defences,

Reference 35

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Observation 6c82d18e-88c6-4fa3-9c72-01f2b97de085 · outbound

This paper cites A comprehensive de- fense framework against model extraction attacks,.

ADS-C: Antidistillation Sampling for Classification A comprehensive de- fense framework against model extraction attacks,

Reference 36

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Observation c2bf0f51-4c38-48b4-99b8-36ea4c10a2e2 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

ADS-C: Antidistillation Sampling for Classification A Survey on Knowledge Distillation of Large Language Models

Reference 37

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This paper cites Undistillable: Making A Nasty Teacher That CANNOT teach students.

ADS-C: Antidistillation Sampling for Classification Undistillable: Making A Nasty Teacher That CANNOT teach students

Reference 38

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Observation 0d407a49-b5c6-4a81-b006-3c0def660f26 · outbound

This paper cites Adversarial sparse teacher: Defense against distillation-based model stealing attacks using adversarial examples,.

ADS-C: Antidistillation Sampling for Classification Adversarial sparse teacher: Defense against distillation-based model stealing attacks using adversarial examples,

Reference 39

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Observation 0fb3e5e6-d761-4b21-8d45-5d9f5db41252 · outbound

This paper cites Defending against model extraction attacks with ood feature learning and decision boundary confusion,.

ADS-C: Antidistillation Sampling for Classification Defending against model extraction attacks with ood feature learning and decision boundary confusion,

Reference 40

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Observation 9dde8944-2c60-4c02-844d-c0fd23468225 · outbound

This paper cites MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models.

ADS-C: Antidistillation Sampling for Classification MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models

Reference 41

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This paper cites Prada: protecting against dnn model stealing attacks,.

ADS-C: Antidistillation Sampling for Classification Prada: protecting against dnn model stealing attacks,

Reference 42

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Observation 0617338a-0c30-436a-9cbe-8d65401eb0ec · outbound

This paper cites MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction.

ADS-C: Antidistillation Sampling for Classification MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction

Reference 43

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This paper cites Radep: A resilient adaptive defense framework against model extraction attacks,.

ADS-C: Antidistillation Sampling for Classification Radep: A resilient adaptive defense framework against model extraction attacks,

Reference 44

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Observation 32f0f09a-03e6-416e-82f4-dda1af74a79e · outbound

This paper cites The Sybil attack,.

ADS-C: Antidistillation Sampling for Classification The Sybil attack,

Reference 45

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This paper cites Cloudleak: Large-scale deep learning models stealing through adversarial exam- ples.

ADS-C: Antidistillation Sampling for Classification Cloudleak: Large-scale deep learning models stealing through adversarial exam- ples

Reference 46

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This paper cites Embedding water- marks into deep neural networks,.

ADS-C: Antidistillation Sampling for Classification Embedding water- marks into deep neural networks,

Reference 47

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This paper cites Entangled watermarks as a defense against model extraction,.

ADS-C: Antidistillation Sampling for Classification Entangled watermarks as a defense against model extraction,

Reference 48

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This paper cites Dawn: Dynamic adversarial watermarking of neural networks,.

ADS-C: Antidistillation Sampling for Classification Dawn: Dynamic adversarial watermarking of neural networks,

Reference 49

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This paper cites Defense against model extraction attack by bayesian active watermarking,.

ADS-C: Antidistillation Sampling for Classification Defense against model extraction attack by bayesian active watermarking,

Reference 50

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Observation ae3fd64b-c402-48bc-bb9b-3970dddf2595 · outbound

This paper cites Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks.

ADS-C: Antidistillation Sampling for Classification Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks

Reference 51

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This paper cites {ModelGuard}:{Information-Theoretic}defense against model extraction attacks,.

ADS-C: Antidistillation Sampling for Classification {ModelGuard}:{Information-Theoretic}defense against model extraction attacks,

Reference 52

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This paper cites Efficient model stealing defense with noise transition matrix,.

ADS-C: Antidistillation Sampling for Classification Efficient model stealing defense with noise transition matrix,

Reference 53

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This paper cites Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective.

ADS-C: Antidistillation Sampling for Classification Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective

Reference 54

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Observation 78af3e37-fbf5-486c-96b9-f4205ec2a98b · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

ADS-C: Antidistillation Sampling for Classification Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 55

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This paper cites Adaptive temperature scal- ing for robust calibration of deep neural networks,.

ADS-C: Antidistillation Sampling for Classification Adaptive temperature scal- ing for robust calibration of deep neural networks,

Reference 56

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This paper cites Sample- dependent adaptive temperature scaling for improved calibration,.

ADS-C: Antidistillation Sampling for Classification Sample- dependent adaptive temperature scaling for improved calibration,

Reference 57

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Observation a1789fe2-3669-4317-945f-61b7b53b8766 · outbound

This paper cites Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks.

ADS-C: Antidistillation Sampling for Classification Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks

Reference 58

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This paper cites Asymmetric temperature scaling makes larger networks teach well again,.

ADS-C: Antidistillation Sampling for Classification Asymmetric temperature scaling makes larger networks teach well again,

Reference 59

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This paper cites Research computing services,.

ADS-C: Antidistillation Sampling for Classification Research computing services,

Reference 60

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Observation 36e8e748-33aa-42f4-b410-488acedd8191 · outbound

This paper cites Available: https://www.anthropic.com/news/ detecting-and-preventing-distillation-attacks.

ADS-C: Antidistillation Sampling for Classification Available: https://www.anthropic.com/news/ detecting-and-preventing-distillation-attacks

Reference 2026

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