Fetching the paper…
Reading the bibliography…
Good data stewardship requires removal of data at the request of the data's owner.
Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J. G., Salakhutdinov, R., and Le, Q. V. (2019) · 1906
Earlier work this paper cites.
Making AI forget you: Data deletion in machine learning
Ginart, A., Guan, M. Y., Valiant, G., and Zou, J. (2019) · 1907
Earlier work this paper cites.
Roberta: A robustly optimized BERT pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019) · 1907
Earlier work this paper cites.
Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N. (2019) · 1912
Earlier work this paper cites.
Residuals and influence in regression
Cook, R. D. and Weisberg, S. (1982) · 1982
Earlier work this paper cites.
On the limited memory bfgs method for large scale optimization
Liu, D. C. and Nocedal, J. (1989) · 1989
Earlier work this paper cites.
Downdating the singular value decomposition
Gu, M. and Eisenstat, S. C. (1995) · 1995
Earlier work this paper cites.
Incremental and decremental support vector machine learning
Cauwenberghs, G. and Poggio, T. A. (2000) · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F. (2009) · 2009
Earlier work this paper cites.
Multiple incremental decremental learning of support vector machines
Karasuyama, M. and Takeuchi, I. (2009) · 2009
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D. (2011) · 2011
Earlier work this paper cites.
Differential privacy
Dwork, C. (2011) · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
Biggio, B., Nelson, B., and Laskov, P. (2012) · 2012
Cited alongside, same era.
Near-optimal differentially private principal components
Chaudhuri, K., Sarwate, A. D., and Sinha, K. (2012) · 2012
Cited alongside, same era.
Incremental and decremental training for linear classification
Tsai, C., Lin, C., and Lin, C. (2014) · 2014
Cited alongside, same era.
Towards making systems forget with machine unlearning
Cao, Y. and Yang, J. (2015) · 2015
Cited alongside, same era.
Fast differentially private matrix factorization
Liu, Z., Wang, Y.-X., and Smola, A. (2015) · 2015
Cited alongside, same era.
Faster R-CNN: towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R. B., and Sun, J. (2015) · 2015
Cited alongside, same era.
Mask R-CNN
He, K., Gkioxari, G., Dollár, P., and Girshick, R. B. (2017) · 2017
Later among the works it cites.
Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P. (2017) · 2017
Later among the works it cites.
Deep learning with dynamic computation graphs
Looks, M., Herreshoff, M., Hutchins, D., and Norvig, P. (2017) · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R. B., Dollár, P., Tu, Z., and He, K. (2017) · 2017
Later among the works it cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017) · 2017
Later among the works it cites.
Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R. B., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D. (2015) · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I. J., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. (2016) · 2016
Cited alongside, same era.
Yfcc100m: The new data in multimedia research
Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J. (2016) · 2016
Cited alongside, same era.
Towards universal paraphrastic sentence embeddings
Wieting, J., Bansal, M., Gimpel, K., and Livescu, K. (2016) · 2016
Cited alongside, same era.
Quo vadis, action recognition? A new model and the kinetics dataset
Carreira, J. and Zisserman, A. (2017) · 2017
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S. (2018) · 2018
Later among the works it cites.
The secret sharer: Measuring unintended neural network memorization & extracting secrets
Carlini, N., Liu, C., Kos, J., Erlingsson, U., and Song, D. (2019) · 2019
Closest in time.
Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J. G., Le, Q. V., and Salakhutdinov, R. (2019) · 2019
Closest in time.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019) · 2019
Closest in time.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R. (2019) · 2019
Closest in time.