Fetching the paper…
Reading the bibliography…
Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility.
The mnist database of handwritten digits
Y. LeCun and C. Cortes · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A Krizhevsky · 2009
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. E. Hinton · 2015
Earlier work this paper cites.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Earlier work this paper cites.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
Earlier work this paper cites.
Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2017
Earlier work this paper cites.
Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, Eider Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas · 2017
Earlier work this paper cites.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. Suematsu, J. Tan, , Q. V. Le, and A. Kurakin · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
The complexity of differential privacy
Salil Vadhan · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
H. Cai, L. Zhu, and S. Han · 2018
Cited alongside, same era.
Concentrated differentially private gradient descent with adaptive per-iteration privacy budget
Adaclip: Adaptive clipping for private sgd
V. Pichapati, A. T. Suresh, F. Yu, S. J. Reddi, and S. Kumar · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, , and Q. V. Le · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
Later among the works it cites.
Differentially private learning with adaptive clipping
O. Thakkar, G. Andrew, and H. B. McMahan · 2019
Later among the works it cites.
Subsampled rényi differential privacy and analytical moments accountant
Y. Wang, B. Balle, and S. Kasiviswanathan · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jaewoo Lee and Daniel Kifer · 2018
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Cited alongside, same era.
Group normalization
Y. Wu and K. He · 2018
Cited alongside, same era.
Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, , and Q. V. Le · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
P3sgd: Patient privacy preserving sgd for regularizing deep cnns in pathological image classification
B. Wu, S. Zhao, G. Sun, X. Zhang, Z. Su, C. Zeng, and Z. Liu · 2019
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
Later among the works it cites.
Ethical algorithm design
Michael Kearns and Aaron Roth · 2020
Later among the works it cites.
Scaling up differentially private deep learning with fast per-example gradient clipping
Jaewoo Lee and Daniel Kifer · 2020
Later among the works it cites.
Enabling fast differentially private sgd via just-in-time compilation and vectorization
Pranav Subramani, Nicholas Vadivelu, and Gautam Kamath · 2020
Later among the works it cites.
Differentially private learning with small public data
J. Wang and Z. Zhou · 2020
Later among the works it cites.
Tempered sigmoid activations for deep learning with differential privacy
N. Papernot, A. Thakurta, S. Song, S. Chien, and Ú. Erlingsson · 2021
Closest in time.
Differentially private learning needs better features (or much more data)
F. Tramèr and D. Boneh · 2021
Closest in time.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
D. Yu, H. Zhang, W. Chen, and T. Liu · 2021
Closest in time.
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Y. Zhou, Z. Wu, and A. Banerjee · 2021
Closest in time.