Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing | Paper Citation Record

Observation 1b0b88a6-85d9-41a3-b17a-764121951107 · outbound

This paper cites GPT-4 Technical Report.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing GPT-4 Technical Report

Reference 1

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Observation ef826498-a395-4c16-bf6b-3b4f1ca5e41a · outbound

This paper cites Deep-Lock: Secure Authorization for Deep Neural Networks.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Deep-Lock: Secure Authorization for Deep Neural Networks

Reference 2

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Source-reported events for the cited work

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Observation 8a0df5ab-e561-4cd1-9d06-35d6dc928d38 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.905313Z

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Observation 30fe1fb3-7f44-4812-9e31-db7736f82ea6 · outbound

This paper cites Probing classifiers: Promises, shortcomings, and advances,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Probing classifiers: Promises, shortcomings, and advances,

Reference 4

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Observation 3723ab20-1f61-4f41-bfaa-678e6fbd2664 · outbound

This paper cites Representation learning: A review and new perspec- tives,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Representation learning: A review and new perspec- tives,

Reference 5

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Observation 6cfe25cf-5f30-49c8-99ec-33c0190848cc · outbound

This paper cites Military vehicles dataset,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Military vehicles dataset,

Reference 6

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Observation e779c107-bb0d-42c6-baed-e14f69ef7daa · outbound

This paper cites Putting representations to use,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Putting representations to use,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.922044Z

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Observation 037167b9-aa22-401b-9ebe-57d14c735aca · outbound

This paper cites Emerging properties in self-supervised vision trans- formers,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Emerging properties in self-supervised vision trans- formers,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.925923Z

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Observation 67a7c7e6-3574-46d6-97a1-07738aed6082 · outbound

This paper cites Hardware-assisted intellectual property protection of deep learning models,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Hardware-assisted intellectual property protection of deep learning models,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.929912Z

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Observation b0ccf5de-642f-4e4b-9c9d-2d745888f5c9 · outbound

This paper cites Confronting the risks of artificial intelligence,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Confronting the risks of artificial intelligence,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.933607Z

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Observation 40138d19-f415-441b-b7f7-f533b6c011f5 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A simple framework for contrastive learning of visual representations,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.937589Z

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Observation de51bf7a-06a1-46f8-9483-9f321d584fe6 · outbound

This paper cites Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.941532Z

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Observation 464fd281-e5a2-4aa7-a814-1bfc9fa87c13 · outbound

This paper cites An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.945289Z

Source-reported events for the cited work

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Observation a9ace80d-aedc-47ae-818b-8ff58c87b23e · outbound

This paper cites general-image-embedding3,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing general-image-embedding3,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.949702Z

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Observation 201c3e66-5988-407d-98fd-9278e3f12a38 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing An analysis of single-layer networks in unsupervised feature learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.953872Z

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Observation fba5c511-a9ce-47dd-836d-08fe772cc2a7 · outbound

This paper cites Emnist: Extending mnist to handwritten letters,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Emnist: Extending mnist to handwritten letters,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.958210Z

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Observation fab1d02a-02c6-431d-9552-5e2028a0b9d9 · outbound

This paper cites On the Relationship between Self-Attention and Convolutional Layers.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing On the Relationship between Self-Attention and Convolutional Layers

Reference 17

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Observation ddbb787e-a291-49bd-b76b-313e245143ce · outbound

This paper cites Supervised learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Supervised learning,

Reference 18

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Observation cf0d6b29-2189-4d67-96e4-ba599cf4360a · outbound

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

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Imagenet: A large-scale hierarchical image database,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.475075Z

Source-reported events for the cited work

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Observation c8f3f405-f656-44f2-8959-28dfae50d105 · outbound

This paper cites Non-transferable pruning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Non-transferable pruning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.462375Z

Source-reported events for the cited work

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Observation bc391998-6678-4fcd-8092-64fc5d6917c9 · outbound

This paper cites Puma: Secure inference of llama-7b in five minutes,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Puma: Secure inference of llama-7b in five minutes,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.979593Z

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Observation a64992aa-daa1-41c7-91a4-b37f3a1aaf7e · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:41.983988Z

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Observation 84352060-755e-43eb-af4c-0733432fe52c · outbound

This paper cites Decorate the newcomers: Visual domain prompt for continual test time adaptation,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Decorate the newcomers: Visual domain prompt for continual test time adaptation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.449126Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c16fdab8-cc72-4a8f-af08-51032ee74ffb · outbound

This paper cites Unsupervised domain adaptation by backpropagation,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Unsupervised domain adaptation by backpropagation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.435548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80cecf7c-435c-40bb-b1e8-642dbcb78283 · outbound

This paper cites Domain-adversarial training of neural networks,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Domain-adversarial training of neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.423471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd6f7065-447b-4008-a4d0-62810be3fa7e · outbound

This paper cites Tuning pre-trained model via moment probing,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Tuning pre-trained model via moment probing,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.410527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b517945-0b30-413a-9714-b6e054587cb1 · outbound

This paper cites Generative adversarial networks,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Generative adversarial networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.395872Z

Source-reported events for the cited work

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Observation 6afc9006-0fc4-4356-b198-042c3bcb662c · outbound

This paper cites Self-supervised relationship probing,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Self-supervised relationship probing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.382535Z

Source-reported events for the cited work

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Observation 8afff277-4d76-493e-83e3-d8469abd0fef · outbound

This paper cites Sigma: secure gpt inference with function secret sharing,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Sigma: secure gpt inference with function secret sharing,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.369960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 845721a4-08c6-4d91-b3f3-4500f2bd5cab · outbound

This paper cites A survey on vision transformer,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A survey on vision transformer,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.357519Z

Source-reported events for the cited work

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Observation 92ca2f96-f4fd-4723-aee9-71a954fba958 · outbound

This paper cites Pre-trained models: Past, present and future,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Pre-trained models: Past, present and future,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.345476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a513867b-7d6a-43ba-a9f6-5073fe77e25f · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Masked autoencoders are scalable vision learners,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.333641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6f11f7a4-9967-4d7d-8b1b-371f73954e7e · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Momentum contrast for unsupervised visual representation learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.319692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd335490-7d5b-442e-9310-0337a0590c4f · outbound

This paper cites Deep residual learning for image recognition,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Deep residual learning for image recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.306310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c5312e8-37c9-4ef8-8581-83ddc983cfef · outbound

This paper cites Using self-supervised learning can improve model robustness and uncertainty,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Using self-supervised learning can improve model robustness and uncertainty,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.291077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29793f2c-5c5c-4ff4-a3fa-42ca80ca4730 · outbound

This paper cites imagenette.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing imagenette

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.276899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0680ce1-a53b-415a-b003-5e786f19db9d · outbound

This paper cites Fastai: A layered api for deep learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Fastai: A layered api for deep learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.264754Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6871c85f-52cd-408c-930f-7e8685945395 · outbound

This paper cites A database for handwritten text recognition research,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A database for handwritten text recognition research,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.252065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6baa4f4-dc59-4be1-92bf-aededc263840 · outbound

This paper cites A review of deep transfer learning and recent advancements,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A review of deep transfer learning and recent advancements,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.237548Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ed1f7da-f0cd-4a11-b71e-e22c479aa2f0 · outbound

This paper cites Gender and ai: Addressing bias in artifi- cial intelligence,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Gender and ai: Addressing bias in artifi- cial intelligence,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.221042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5db1c377-5b42-405b-b525-46940c7df153 · outbound

This paper cites A survey on contrastive self-supervised learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A survey on contrastive self-supervised learning,

Reference 41

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Observation 349e0ee6-2ea2-4a14-a89a-f7213e9dbd20 · outbound

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

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Entangled watermarks as a defense against model extraction,

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 314dfed5-d34d-4820-af1b-d9843d0a71f5 · outbound

This paper cites Visual prompt tuning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Visual prompt tuning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.181521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9972938e-930f-4ae3-93b7-4c988843f905 · outbound

This paper cites Adam: A method for stochastic optimization,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Adam: A method for stochastic optimization,

Reference 44

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Source-reported events for the cited work

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Observation 10ad5c56-b78a-4ce0-9ee6-e20322b3a0d5 · outbound

This paper cites Auto-Encoding Variational Bayes.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Auto-Encoding Variational Bayes

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.081506Z

Source-reported events for the cited work

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Observation c9bffe2e-f954-4924-b166-db7c38a86a3d · outbound

This paper cites Learning multiple layers of features from tiny images,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Learning multiple layers of features from tiny images,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.085783Z

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Observation 2070d0f8-94f5-45a3-919b-4907d071b64a · outbound

This paper cites Contrastive representation learning: A framework and review,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Contrastive representation learning: A framework and review,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.150616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a34619d1-c79d-4a43-b408-bda7860ee779 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Gradient-based learning applied to document recognition,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.138100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 783228bf-9505-49de-804f-1c2b407748be · outbound

This paper cites Modeldiff: Testing-based dnn similarity comparison for model reuse detection,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Modeldiff: Testing-based dnn similarity comparison for model reuse detection,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.125438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 00852f53-833e-4353-9ea9-d4771ad29e67 · outbound

This paper cites Transtailor: Pruning the pre-trained model for improved transfer learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Transtailor: Pruning the pre-trained model for improved transfer learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.110907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d504c4b-7b82-48d9-87fb-e21b0ca5d033 · outbound

This paper cites Secdeep: Secure and performant on-device deep learning inference framework for mobile and iot devices,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Secdeep: Secure and performant on-device deep learning inference framework for mobile and iot devices,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.097355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 728fd2eb-aa2a-4a83-81c1-992b5bcfe74d · outbound

This paper cites Fault injection attack on deep neural network,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Fault injection attack on deep neural network,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.085084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8b2229ce-1063-4b7d-a530-a94902444101 · outbound

This paper cites Rethinking the Value of Network Pruning.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Rethinking the Value of Network Pruning

Reference 53

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Source-reported events for the cited work

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Observation 38485150-b20c-46f1-8b1a-366540cbeada · outbound

This paper cites Transfer learning from pre-trained models,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Transfer learning from pre-trained models,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.072081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7ac661c-f44d-4aa5-8f2f-8ebb586b6d4a · outbound

This paper cites Is artificial intelligence dangerous? 6 ai risks everyone should know about,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Is artificial intelligence dangerous? 6 ai risks everyone should know about,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.058045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8bf48aa-45cf-48f6-8071-da62586e4ad1 · outbound

This paper cites Data augmentation for improving deep learning in image classification problem,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Data augmentation for improving deep learning in image classification problem,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.044599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b403ba9-f57d-4d6f-a714-debc2ef325ae · outbound

This paper cites Reading digits in natural images with unsupervised feature learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Reading digits in natural images with unsupervised feature learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.030993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ebdfba45-3e5d-431b-af9c-659d46f8b127 · outbound

This paper cites Openai’s embeddings api,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Openai’s embeddings api,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.018381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 47ede02e-de8c-4078-a0d2-60bcf64d5892 · outbound

This paper cites The unsurprising effectiveness of pre-trained vision models for control,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing The unsurprising effectiveness of pre-trained vision models for control,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:43.005398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 10033df4-e5b8-434d-b8b8-7a627f0c0acc · outbound

This paper cites Llm self defense: By self examination, llms know they are being tricked,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Llm self defense: By self examination, llms know they are being tricked,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.991333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 01c5419f-9222-477f-b4ef-ec1fe5b36eac · outbound

This paper cites Early stopping-but when?.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Early stopping-but when?

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.153451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9259669f-ef3d-43cc-a2eb-7638f3b5df88 · outbound

This paper cites Pre-trained models for natural language processing: A survey,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Pre-trained models for natural language processing: A survey,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.969178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba9d2d7a-e874-4af6-a468-f14423c7f392 · outbound

This paper cites Reaas: Enabling adversarially robust downstream classifiers via robust encoder as a service,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Reaas: Enabling adversarially robust downstream classifiers via robust encoder as a service,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.956283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a62aed8a-a424-4c02-a84f-3dc408f404cd · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Learning transferable visual models from natural language supervision,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.943073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 74e9e0a3-af7c-4663-818c-a67fe6cb1bc7 · outbound

This paper cites Bit-flip attack: Crushing neural network with progressive bit search,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Bit-flip attack: Crushing neural network with progressive bit search,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.927989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68c17f12-b4b0-4b5b-878e-691188e64116 · outbound

This paper cites Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.173423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ba5976-6c26-4a22-9ffa-a999d0851bbd · outbound

This paper cites Gaussian mixture models.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Gaussian mixture models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.177649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58bb3853-fb3d-49d6-8195-f568ea954d1c · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing High-resolution image synthesis with latent diffusion models,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.906670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1ba2eee-ab53-44a5-8468-6c06ac41da98 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.893656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bad51658-7aa0-4815-9b7f-b9272e18a3a6 · outbound

This paper cites Financial feature embedding with knowledge representation learning for financial statement fraud detection,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Financial feature embedding with knowledge representation learning for financial statement fraud detection,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.879552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77f48402-b101-4d45-b45c-61fe2bd93795 · outbound

This paper cites A survey on image data augmen- tation for deep learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A survey on image data augmen- tation for deep learning,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.863288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 89ca4128-7e95-42e3-84f9-954c24d9f6cf · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Very deep convolutional networks for large-scale image recognition,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.847849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56fecc20-210f-4c98-8dbe-74adf6153134 · outbound

This paper cites Convolutional neural networks for medical image analysis: Full training or fine tuning?.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Convolutional neural networks for medical image analysis: Full training or fine tuning?

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.832972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8cbc695-b38f-4307-8808-606cceff6b03 · outbound

This paper cites Federated learning from pre-trained models: A contrastive learning approach,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Federated learning from pre-trained models: A contrastive learning approach,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.817226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f73b324a-dea4-4a71-be84-5b86ef2274bf · outbound

This paper cites Ai bill of rights: Algorithmic discrimination protections,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Ai bill of rights: Algorithmic discrimination protections,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.802846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9266638-dc09-4a26-9ffe-1bf8aa813da8 · outbound

This paper cites Visualizing data using t-sne.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Visualizing data using t-sne

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.214145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efbb7529-e495-4a2f-b51e-6e16c9337519 · outbound

This paper cites Pre-trained language models and their applications,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Pre-trained language models and their applications,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.774468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5701998-7dfc-4ec3-ad16-9f71783c5c2e · outbound

This paper cites Model barrier: A compact un- transferable isolation domain for model intellectual property protection,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Model barrier: A compact un- transferable isolation domain for model intellectual property protection,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.761791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cbf042a2-5ab1-4911-a767-d3ca2009bf3f · outbound

This paper cites Non-transferable learning: A new approach for model ownership verification and applicability authorization,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Non-transferable learning: A new approach for model ownership verification and applicability authorization,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.742558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75082e68-0408-4cc1-976d-4d818878c097 · outbound

This paper cites Toxicity Detection with Generative Prompt-based Inference.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Toxicity Detection with Generative Prompt-based Inference

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.231918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e684358-cc80-4f54-8e98-9937630194b1 · outbound

This paper cites A Non-Linear Structural Probe.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing A Non-Linear Structural Probe

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-08-12T17:35:42.394622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0c8a56a-4039-4ebe-87b3-3764b215bc33 · outbound

This paper cites Structured model probing: Empowering efficient transfer learning by structured regularization,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Structured model probing: Empowering efficient transfer learning by structured regularization,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.728666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 777ad74d-77d9-463a-8578-e27ddda0a62b · outbound

This paper cites Fine-grained visual prompting,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Fine-grained visual prompting,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.715031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 65138e8d-ac78-4726-94d7-cc9daec66cf2 · outbound

This paper cites Robust watermarking for deep neural networks via bi-level optimization,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Robust watermarking for deep neural networks via bi-level optimization,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.700579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a18c4ce-bfd7-46c2-ba57-1551b4f1622b · outbound

This paper cites Graph representation learning in bioinformatics: trends, methods and applications,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Graph representation learning in bioinformatics: trends, methods and applications,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.685012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42f8e3cf-6958-4138-9c3e-83495b3269b7 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Florence: A New Foundation Model for Computer Vision

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.261300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f507451-4884-43ea-8244-fc2c8069a887 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing ADADELTA: An Adaptive Learning Rate Method

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T17:35:42.265839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd27c0e0-43b5-4477-8ef2-7eb286860de4 · outbound

This paper cites Protecting intellectual property of deep neural networks with watermarking,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Protecting intellectual property of deep neural networks with watermarking,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.670093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cb8df323-3538-4f4b-ac31-54f024d476a5 · outbound

This paper cites Fault sneaking attack: A stealthy framework for misleading deep neural networks,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Fault sneaking attack: A stealthy framework for misleading deep neural networks,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.657476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ff9d40f-2064-4b0f-a5fe-80877c823d4a · outbound

This paper cites An overview on data representation learning: From traditional feature learning to recent deep learning,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing An overview on data representation learning: From traditional feature learning to recent deep learning,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.643561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9c5d78bb-5670-42ed-b95a-80bf78910565 · outbound

This paper cites Archlock: Locking dnn transferability at the architecture level with a zero-cost binary predictor,.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing Archlock: Locking dnn transferability at the architecture level with a zero-cost binary predictor,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.629441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7188df1e-1f6f-4df4-b41d-49a7ab5445cd · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 92

Resolution: malformed identifier; no resolver link, observed 2026-08-12T17:35:42.288017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4f6ebac-b55c-4f8c-bc60-61886f854cd1 · outbound

This paper cites train- from-scratch.

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing train- from-scratch

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:35:42.616138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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