Fetching the paper…
Reading the bibliography…
Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data.
On measures of entropy and information
A. Rényi et al · 1961
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Fundamental bound on the reliability of quantum information transmission
N. Sharma and N. A. Warsi · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. D. Sarwate · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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 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.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2017
Earlier work this paper cites.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Earlier work this paper cites.
Renyi differential privacy
I. Mironov · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Earlier work this paper cites.
Large batch training of convolutional networks
Y. You, I. Gitman, and B. Ginsburg · 2017
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
The inaturalist species classification and detection dataset
G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2018
Cited alongside, same era.
Unified perceptual parsing for scene understanding
T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
Cited alongside, same era.
Rethinking imagenet pre-training
K. He, R. Girshick, and P. Dollár · 2019
Cited alongside, same era.
R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
I. Mironov, K. Talwar, and L. Zhang · 2019
Cited alongside, same era.
Detectron2
Exploring simple siamese representation learning
X. Chen and K. He · 2021
Later among the works it cites.
functorch: Jax-like composable function transforms for pytorch
R. Z. Horace He · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Later among the works it cites.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
C. Schuhmann, R. Vencu, R. Beaumont, R. Kaczmarczyk, C. Mullis, A. Katta, T. Coombes, J. Jitsev, and A. Komatsuzaki · 2021
Later among the works it cites.
Opacus: User-friendly differential privacy library in pytorch
A. Yousefpour, I. Shilov, A. Sablayrolles, D. Testuggine, K. Prasad, M. Malek, J. Nguyen, S. Ghosh, A. Bharadwaj, J. Zhao, et al · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick · 2019
Cited alongside, same era.
Semantic understanding of scenes through the ade20k dataset
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy
B. Balle, G. Barthe, M. Gaboardi, J. Hsu, and T. Sato · 2020
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
Cited alongside, same era.
Differentially private fine-tuning of language models
D. Yu, S. Naik, A. Backurs, S. Gopi, H. A. Inan, G. Kamath, J. Kulkarni, Y. T. Lee, A. Manoel, L. Wutschitz, et al · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny · 2021
Later among the works it cites.
Procedural image programs for representation learning
M. Baradad, R. Chen, J. Wulff, T. Wang, R. Feris, A. Torralba, and P. Isola · 2022
Later among the works it cites.
Scalable and efficient training of large convolutional neural networks with differential privacy
Z. Bu, J. Mao, and S. Xu · 2022
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
S. De, L. Berrada, J. Hayes, S. L. Smith, and B. Balle · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2022
Later among the works it cites.
Vision transformers provably learn spatial structure
S. Jelassi, M. Sander, and Y. Li · 2022
Later among the works it cites.
Toward training at imagenet scale with differential privacy
A. Kurakin, S. Song, S. Chien, R. Geambasu, A. Terzis, and A. Thakurta · 2022
Later among the works it cites.
Slip: Self-supervision meets language-image pre-training
N. Mu, A. Kirillov, D. Wagner, and S. Xie · 2022
Later among the works it cites.
Tan without a burn: Scaling laws of dp-sgd
T. Sander, P. Stock, and A. Sablayrolles · 2022
Later among the works it cites.
Simmim: A simple framework for masked image modeling
Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu · 2022
Later among the works it cites.
A cookbook of self-supervised learning
R. Balestriero, M. Ibrahim, V. Sobal, A. S. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. G. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, and M. Goldblum · 2023
Closest in time.
Foundation models and fair use
P. Henderson, X. Li, D. Jurafsky, T. Hashimoto, M. A. Lemley, and P. Liang · 2023
Closest in time.
Do ssl models have déjà vu? a case of unintended memorization in self-supervised learning
C. Meehan, F. Bordes, P. Vincent, K. Chaudhuri, and C. Guo · 2023
Closest in time.
Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
Closest in time.