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It is commonplace to produce application-specific models by fine-tuning large pre-trained models using a small bespoke dataset.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Regularizing and optimizing lstm language models
Merity, S., Keskar, N. S., and Socher, R · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D. X · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., and Jégou, H · 2019
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Quantifying membership inference vulnerability via generalization gap and other model metrics
Bentley, J. W., Gibney, D., Hoppenworth, G., and Jha, S. K · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Inverting gradients - how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Black, S., Gao, L., Wang, P., Leahy, C., and Biderman, S · 2021
Earlier work this paper cites.
Extracting training data from large language models
Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., and Raffel, C · 2021
Cited alongside, same era.
Label-only membership inference attacks
Choquette-Choo, C. A., Tramer, F., Carlini, N., and Papernot, N · 2021
Cited alongside, same era.
Introducing Android’s Private Compute Services, September 2021
Frey, S · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., and Wallace, E · 2023
Later among the works it cites.
Reproducible scaling laws for contrastive language-image learning
Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., and Jitsev, J · 2023
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Privacy side channels in machine learning systems
Debenedetti, E., Severi, G., Carlini, N., Choquette-Choo, C. A., Jagielski, M., Nasr, M., Wallace, E., and Tramèr, F · 2023
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Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
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Are diffusion models vulnerable to membership inference attacks?
Duan, J., Kong, F., Wang, S., Shi, X., and Xu, K · 2023
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Yin, H., Mallya, A., Vahdat, A., Alvarez, J. M., Kautz, J., and Molchanov, P · 2021
Cited alongside, same era.
Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
Cited alongside, same era.
Knowledge neurons in pretrained transformers
Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., and Wei, F · 2022
Cited alongside, same era.
Robbing the fed: Directly obtaining private data in federated learning with modified models
Fowl, L. H., Geiping, J., Czaja, W., Goldblum, M., and Goldstein, T · 2022
Cited alongside, same era.
Handcrafted backdoors in deep neural networks
Hong, S., Carlini, N., and Kurakin, A · 2022
Cited alongside, same era.
Truth serum: Poisoning machine learning models to reveal their secrets
Tramèr, F., Shokri, R., Joaquin, A. S., Le, H. M., Jagielski, M., Hong, S., and Carlini, N · 2022
Cited alongside, same era.
Fishing for user data in large-batch federated learning via gradient magnification
Wen, Y., Geiping, J. A., Fowl, L., Goldblum, M., and Goldstein, T · 2022
Cited alongside, same era.
Decepticons: Corrupted transformers breach privacy in federated learning for language models
Fowl, L. H., Geiping, J., Reich, S., Wen, Y., Czaja, W., Goldblum, M., and Goldstein, T · 2023
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Neftune: Noisy embeddings improve instruction finetuning
Jain, N., Chiang, P.-y., Wen, Y., Kirchenbauer, J., Chu, H.-M., Somepalli, G., Bartoldson, B. R., Kailkhura, B., Schwarzschild, A., Saha, A., et al · 2023
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Mimic-iv, a freely accessible electronic health record dataset
Johnson, A. E., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., et al · 2023
Later among the works it cites.
A watermark for large language models
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., and Goldstein, T · 2023
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How hard is trojan detection in DNNs? fooling detectors with evasive trojans, 2023
Mazeika, M., Zou, A., Arora, A., Pleskov, P., Song, D., Hendrycks, D., Li, B., and Forsyth, D · 2023
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Morris, J. X., Zhao, W., Chiu, J. T., Shmatikov, V., and Rush, A. M · 2023
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Fact sheet: President biden issues executive order on safe, secure, and trustworthy artificial intelligence, 10 2023
The White House · 2023
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Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
Wang, G., Liu, X., Ying, Z., Yang, G., Chen, Z., Liu, Z., Zhang, M., Yan, H., Lu, Y., Gao, Y., et al · 2023
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Canary in a coalmine: Better membership inference with ensembled adversarial queries
Wen, Y., Bansal, A., Kazemi, H., Borgnia, E., Goldblum, M., Geiping, J., and Goldstein, T · 2023
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Privacy backdoors: Stealing data with corrupted pretrained models
Feng, S. and Tramèr, F · 2024
Closest in time.
Sleeper agents: Training deceptive llms that persist through safety training
Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al · 2024
Closest in time.