Vikas Verma
Title
Cited by
Cited by
Year
Manifold Mixup: Better Representations by Interpolating Hidden States
V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, D Lopez-Paz, ...
International Conference on Machine Learning, 6438-6447, 2019
1092019
Interpolation consistency training for semi-supervised learning
V Verma, A Lamb, J Kannala, Y Bengio, D Lopez-Paz
arXiv preprint arXiv:1903.03825, 2019
1072019
Residual connections encourage iterative inference
S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio
arXiv preprint arXiv:1710.04773, 2017
532017
Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
V Verma, A Lamb, C Beckham, A Courville, I Mitliagkis, Y Bengio
stat 1050, 13, 2018
402018
Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
FY Sun, J Hoffmann, V Verma, J Tang
arXiv preprint arXiv:1908.01000, 2019
282019
On adversarial mixup resynthesis
C Beckham, S Honari, V Verma, AM Lamb, F Ghadiri, RD Hjelm, Y Bengio, ...
Advances in neural information processing systems, 4346-4357, 2019
19*2019
Interpolated adversarial training: Achieving robust neural networks without sacrificing too much accuracy
A Lamb, V Verma, J Kannala, Y Bengio
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019
122019
Deep semi-random features for nonlinear function approximation
K Kawaguchi, B Xie, V Verma, L Song
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
122018
Graphmix: Regularized training of graph neural networks for semi-supervised learning
V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang
arXiv preprint arXiv:1909.11715, 2019
112019
Manifold mixup: Learning better representations by interpolating hidden states
V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ...
102018
Towards understanding generalization via analytical learning theory
K Kawaguchi, Y Bengio, V Verma, LP Kaelbling
arXiv preprint arXiv:1802.07426, 2018
9*2018
Towards understanding generalization in gradient-based meta-learning
S Guiroy, V Verma, C Pal
arXiv preprint arXiv:1907.07287, 2019
72019
Method and apparatus for determining similarity information for users of a network
V Verma
US Patent 9,373,128, 2016
72016
Modularity Matters: Learning Invariant Relational Reasoning Tasks
J Jo, V Verma, Y Bengio
arXiv preprint arXiv:1806.06765, 2018
52018
Interpolated adversarial training: Achieving robust neural networks without sacrificing accuracy
A Lamb, V Verma, J Kannala, Y Bengio
arXiv preprint arXiv:1906.06784, 2019
32019
User service prediction in a communication network
V Verma, V Huang
US Patent App. 15/100,950, 2016
32016
Patchup: A regularization technique for convolutional neural networks
M Faramarzi, M Amini, A Badrinaaraayanan, V Verma, S Chandar
arXiv preprint arXiv:2006.07794, 2020
22020
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
A Lamb, S Ozair, V Verma, D Ha
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 952-961, 2020
12020
Interpolation-based semi-supervised learning for object detection
J Jeong, V Verma, M Hyun, J Kannala, N Kwak
arXiv preprint arXiv:2006.02158, 2020
2020
Identifying influence paths in a communication network
NH Kumar, R Balakrishnan, V Verma
US Patent 9,680,732, 2017
2017
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