Fetching the paper…
Reading the bibliography…
Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines.
D. Cohn, L. Atlas, and R. Ladner, “Improving generalization with active learning,” Machine learning , vol. 15, no. 2, pp. 201–221, 1994
1994
Earlier work this paper cites.
P. L. Bartlett, “For valid generalization the size of the weights is more important than the size of the network,” in Advances in neural information processing systems , 1997, pp. 134–140
1997
Earlier work this paper cites.
P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE transactions on Information Theory , vol. 44, no. 2, pp. 525–536, 1998
1998
Earlier work this paper cites.
A. Pinkus, “Approximation theory of the mlp model in neural networks,” Acta numerica , vol. 8, pp. 143–195, 1999
1999
Earlier work this paper cites.
S. Tong and D. Koller, “Support vector machine active learning with applications to text classification,” Journal of machine learning research , vol. 2, no. Nov, pp. 45–66, 2001
2001
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep., 2009
2009
Earlier work this paper cites.
S. Dasgupta, “Two faces of active learning,” Theoretical computer science , vol. 412, no. 19, pp. 1767–1781, 2011
2011
Earlier work this paper cites.
——, “Active learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 6, no. 1, pp. 1–114, 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
B. Neyshabur, R. Tomioka, and N. Srebro, “Norm-based capacity control in neural networks,” in Conference on Learning Theory , 2015, pp. 1376–1401
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Cited alongside, same era.
Y. Gal, R. Islam, and Z. Ghahramani, “Deep bayesian active learning with image data,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 1183–1192
2017
Cited alongside, same era.
K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin, “Cost-effective active learning for deep image classification,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 27, no. 12, pp. 2591–2600, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Belkin, D. J. Hsu, and P. Mitra, “Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate,” in Advances in Neural Information Processing Systems , 2018, pp. 2300–2311
2018
Later among the works it cites.
2018
Later among the works it cites.
N. Golowich, A. Rakhlin, and O. Shamir, “Size-independent sample complexity of neural networks,” in Conference On Learning Theory , 2018, pp. 297–299
2018
Later among the works it cites.
S. Arora, R. Ge, B. Neyshabur, and Y. Zhang, “Stronger generalization bounds for deep nets via a compression approach,” in International Conference on Machine Learning , 2018, pp. 254–263
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…
P. L. Bartlett, D. J. Foster, and M. J. Telgarsky, “Spectrally-normalized margin bounds for neural networks,” in Advances in Neural Information Processing Systems , 2017, pp. 6240–6249
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Ma, R. Bassily, and M. Belkin, “The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning,” in International Conference on Machine Learning , 2018, pp. 3331–3340
2018
Cited alongside, same era.
M. Belkin, S. Ma, and S. Mandal, “To understand deep learning we need to understand kernel learning,” in International Conference on Machine Learning , 2018, pp. 540–548
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
P. H. P. Savarese, I. Evron, D. Soudry, and N. Srebro, “How do infinite width bounded norm networks look in function space?” in Conference on Learning Theory, COLT 2019, 25-28 June 2019, Phoenix, AZ, USA , 2019, pp. 2667–2690
2019
Closest in time.
2019
Closest in time.