Q UANTIFYING M EMORIZATION A CROSS N EURAL L ANGUAGE M ODELS
暂无分享,去创建一个
Florian Tramèr | Nicholas Carlini | Katherine Lee | Daphne Ippolito | Matthew Jagielski | Chiyuan Zhang
[1] Xi Victoria Lin,et al. OPT: Open Pre-trained Transformer Language Models , 2022, ArXiv.
[2] Colin Raffel,et al. Deduplicating Training Data Mitigates Privacy Risks in Language Models , 2022, ICML.
[3] Florian Tramèr,et al. What Does it Mean for a Language Model to Preserve Privacy? , 2022, FAccT.
[4] Daphne Ippolito,et al. Counterfactual Memorization in Neural Language Models , 2021, ArXiv.
[5] Asli Celikyilmaz,et al. How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN , 2021, TACL.
[6] Badih Ghazi,et al. Large-Scale Differentially Private BERT , 2021, EMNLP.
[7] Nicholas Carlini,et al. Deduplicating Training Data Makes Language Models Better , 2021, ACL.
[8] Wojciech Zaremba,et al. Evaluating Large Language Models Trained on Code , 2021, ArXiv.
[9] Stella Biderman,et al. GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow , 2021 .
[10] Noam M. Shazeer,et al. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity , 2021, J. Mach. Learn. Res..
[11] Milad Nasr,et al. Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning , 2021, 2021 IEEE Symposium on Security and Privacy (SP).
[12] Charles Foster,et al. The Pile: An 800GB Dataset of Diverse Text for Language Modeling , 2020, ArXiv.
[13] Colin Raffel,et al. Extracting Training Data from Large Language Models , 2020, USENIX Security Symposium.
[14] H. Brendan McMahan,et al. Training Production Language Models without Memorizing User Data , 2020, ArXiv.
[15] Dietrich Klakow,et al. Investigating the Impact of Pre-trained Word Embeddings on Memorization in Neural Networks , 2020, TDS.
[16] Vitaly Feldman,et al. What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation , 2020, NeurIPS.
[17] Jonathan Ullman,et al. Auditing Differentially Private Machine Learning: How Private is Private SGD? , 2020, NeurIPS.
[18] Swaroop Ramaswamy,et al. Understanding Unintended Memorization in Federated Learning , 2020, ArXiv.
[19] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[20] David Evans,et al. Evaluating Differentially Private Machine Learning in Practice , 2019, USENIX Security Symposium.
[21] Nikita Borisov,et al. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations , 2018, CCS.
[22] Úlfar Erlingsson,et al. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.
[23] Peter Henderson,et al. Ethical Challenges in Data-Driven Dialogue Systems , 2017, AIES.
[24] Somesh Jha,et al. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting , 2017, 2018 IEEE 31st Computer Security Foundations Symposium (CSF).
[25] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[26] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[27] Ian Goodfellow,et al. Deep Learning with Differential Privacy , 2016, CCS.
[28] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[29] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.