Srinadh Bhojanapalli
Srinadh Bhojanapalli
Research Scientist, Google Research
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Exploring generalization in deep learning
B Neyshabur, S Bhojanapalli, D McAllester, N Srebro
arXiv preprint arXiv:1706.08947, 2017
6662017
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
B Neyshabur, S Bhojanapalli, N Srebro
arXiv preprint arXiv:1707.09564, 2017
3402017
Global optimality of local search for low rank matrix recovery
S Bhojanapalli, B Neyshabur, N Srebro
Advances in Neural Information Processing Systems, 3873-3881, 2016
3252016
Towards understanding the role of over-parametrization in generalization of neural networks
B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro
arXiv preprint arXiv:1805.12076, 2018
307*2018
Large batch optimization for deep learning: Training bert in 76 minutes
Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ...
arXiv preprint arXiv:1904.00962, 2019
2552019
Implicit regularization in matrix factorization
S Gunasekar, B Woodworth, S Bhojanapalli, B Neyshabur, N Srebro
2018 Information Theory and Applications Workshop (ITA), 1-10, 2018
2252018
Dropping convexity for faster semi-definite optimization
S Bhojanapalli, A Kyrillidis, S Sanghavi
Conference on Learning Theory, 530-582, 2016
1512016
Coherent matrix completion.
Y Chen, S Bhojanapalli, S Sanghavi, R Ward
arXiv preprint arXiv:1306.2979, 2013
1332013
Universal matrix completion
S Bhojanapalli, P Jain
International Conference on Machine Learning, 1881-1889, 2014
972014
Completing any low-rank matrix, provably
Y Chen, S Bhojanapalli, S Sanghavi, R Ward
The Journal of Machine Learning Research 16 (1), 2999-3034, 2015
582015
Stabilizing GAN training with multiple random projections
B Neyshabur, S Bhojanapalli, A Chakrabarti
arXiv preprint arXiv:1705.07831, 2017
552017
Does label smoothing mitigate label noise?
M Lukasik, S Bhojanapalli, A Menon, S Kumar
International Conference on Machine Learning, 6448-6458, 2020
522020
Are transformers universal approximators of sequence-to-sequence functions?
C Yun, S Bhojanapalli, AS Rawat, SJ Reddi, S Kumar
arXiv preprint arXiv:1912.10077, 2019
512019
Tighter low-rank approximation via sampling the leveraged element
S Bhojanapalli, P Jain, S Sanghavi
Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014
432014
A new sampling technique for tensors
S Bhojanapalli, S Sanghavi
arXiv preprint arXiv:1502.05023, 2015
322015
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
S Bhojanapalli, N Boumal, P Jain, P Netrapalli
Conference On Learning Theory, 3243-3270, 2018
282018
Completing any low-rank matrix, provably
Y Chen, S Bhojanapalli, S Sanghavi, R Ward
arXiv preprint arXiv:1306.2979, 2013
262013
Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems
D Park, A Kyrillidis, S Bhojanapalli, C Caramanis, S Sanghavi
arXiv preprint arXiv:1606.01316, 2016
252016
Provable non-convex projected gradient descent for a class of constrained matrix optimization problems
D Park, A Kyrillidis, S Bhojanapalli, C Caramanis, S Sanghavi
stat 1050, 4, 2016
182016
Understanding robustness of transformers for image classification
S Bhojanapalli, A Chakrabarti, D Glasner, D Li, T Unterthiner, A Veit
arXiv preprint arXiv:2103.14586, 2021
172021
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