Fetching the paper…
Reading the bibliography…
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
M. C. Mozer and P. Smolensky · 1988
Earlier work this paper cites.
Characterising bias in compressed models
S. Hooker, N. Moorosi, G. Clark, S. Bengio, and E. L. Denton · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Ng · 2011
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Explaining and harnessing adversarial examples, 2014
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural networks
J. T. S. Han, J. Pool and W. J. Dally · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, E. Price, and N. Srebro · 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.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. McDaniel, and I. Goodfellow · 2016
Earlier work this paper cites.
Fairness in machine learning
S. Barocas, M. Hardt, and A. Narayanan · 2017
Earlier work this paper cites.
Age progression/regression by conditional adversarial autoencoder
Z. Zhang, Y. Song, and H. Qi · 2017
Earlier work this paper cites.
Sparse dnns with improved adversarial robustness
Y. Guo, C. Zhang, C. Zhang, and Y. Chen · 2018
Earlier work this paper cites.
A systematic dnn weight pruning framework using alternating direction method of multipliers
T. Zhang, S. Ye, K. Zhang, J. Tang, W. Wen, M. Fardad, and Y. Wang · 2018
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
Cited alongside, same era.
Sipping neural networks: Sensitivity-informed provable pruning of neural networks
C. Baykal, L. Liebenwein, I. Gilitschenski, D. Feldman, and D. Rus · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
J. Cohen, E. Rosenfeld, and Z. Kolter · 2019
Cited alongside, same era.
Towards compact and robust deep neural networks
V. Sehwag, S. Wang, P. Mittal, and S. Jana · 2019
Cited alongside, same era.
Inherent tradeoffs in learning fair representations
H. Zhao and G. Gordon · 2019
Cited alongside, same era.
Combining weight pruning and knowledge distillation for cnn compression
N. Aghli and E. Ribeiro · 2021
Later among the works it cites.
The low-resource double bind: An empirical study of pruning for low-resource machine translation
O. Ahia, J. Kreutzer, and S. Hooker · 2021
Later among the works it cites.
Simon says: Evaluating and mitigating bias in pruned neural networks with knowledge distillation
C. Blakeney, N. Huish, Y. Yan, and Z. Zong · 2021
Later among the works it cites.
What do compressed large language models forget? robustness challenges in model compression
M. Du, S. Mukherjee, Y. Cheng, M. Shokouhi, X. Hu, and A. H. Awadallah · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan · 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…
D. Blalock, J. J. G. Ortiz, J. Frankle, and J. Guttag · 2020
Cited alongside, same era.
Fairness in machine learning: A survey
S. Caton and C. Haas · 2020
Cited alongside, same era.
Lagrangian duality for constrained deep learning
F. Fioretto, P. Van Hentenryck, T. Mak, C. Tran, F. Baldo, and M. Lombardi · 2020
Cited alongside, same era.
Going beyond classification accuracy metrics in model compression
V. Joseph, S. A. Siddiqui, A. Bhaskara, G. Gopalakrishnan, S. Muralidharan, M. Garland, S. Ahmed, and A. R. Dengel · 2020
Cited alongside, same era.
M. Paganini · 2020
Cited alongside, same era.
Fair decision making using privacy-protected data
D. Pujol, R. McKenna, S. Kuppam, M. Hay, A. Machanavajjhala, and G. Miklau · 2020
Cited alongside, same era.
Comparing rewinding and fine-tuning in neural network pruning
A. Renda, J. Frankle, and M. Carbin · 2020
Cited alongside, same era.
Dp-sgd vs pate: Which has less disparate impact on model accuracy?
A. Uniyal, R. Naidu, S. Kotti, S. Singh, P. J. Kenfack, F. Mireshghallah, and A. Trask · 2021
Later among the works it cites.
Beyond preserved accuracy: Evaluating loyalty and robustness of BERT compression
C. Xu, W. Zhou, T. Ge, K. Xu, J. McAuley, and F. Wei · 2021
Later among the works it cites.
Differential privacy and fairness in decisions and learning tasks: A survey
F. Fioretto, C. Tran, P. V. Hentenryck, and K. Zhu · 2022
Closest in time.
Recall distortion in neural network pruning and the undecayed pruning algorithm
A. Good, J. Lin, X. Yu, H. Sieg, M. Ferguson, S. Zhe, J. Wieczorek, and T. Serra · 2022
Closest in time.
W. Toussaint, A. Mathur, F. Kawsar, and A. Y. Ding · 2022
Closest in time.
Can model compression improve nlp fairness
G. Xu and Q. Hu · 2022
Closest in time.
The rich get richer: Disparate impact of semi-supervised learning
Z. Zhu, T. Luo, and Y. Liu · 2022
Closest in time.