Vikas Verma
Vikas Verma
Aalto University/ MILA/ Google Brain
Geverifieerd e-mailadres voor google.com
Geciteerd door
Geciteerd door
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
Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
FY Sun, J Hoffmann, V Verma, J Tang
ICLR 2020 Spotlight, 2019
Interpolation consistency training for semi-supervised learning
V Verma, A Lamb, J Kannala, Y Bengio, D Lopez-Paz
IJCAI 2019, 2019
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
V Verma, M Qu, K Kawaguchi, A Lamb, Y Bengio, J Kannala, J Tang
AAAI 2021, 2019
Residual connections encourage iterative inference
S Jastrzębski, D Arpit, N Ballas, V Verma, T Che, Y Bengio
ICLR 2018, 2017
Towards Domain-Agnostic Contrastive Learning
V Verma, MT Luong, K Kawaguchi, H Pham, QV Le
ICML2021, 2020
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
Interpolation-based semi-supervised learning for object detection
J Jeong, V Verma, M Hyun, J Kannala, N Kwak
CVPR 2021, 2020
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
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
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
Patchup: A regularization technique for convolutional neural networks
M Faramarzi, M Amini, A Badrinaaraayanan, V Verma, S Chandar
AAAI 2022, 2020
Towards understanding generalization in gradient-based meta-learning
S Guiroy, V Verma, C Pal
arXiv preprint arXiv:1907.07287, 2019
Deep semi-random features for nonlinear function approximation
K Kawaguchi, B Xie, V Verma, L Song
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
Manifold mixup: Learning better representations by interpolating hidden states
V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ...
Towards understanding generalization via analytical learning theory
K Kawaguchi, Y Bengio, V Verma, LP Kaelbling
arXiv preprint arXiv:1802.07426, 2018
Method and apparatus for determining similarity information for users of a network
V Verma
US Patent 9,373,128, 2016
Modularity matters: learning invariant relational reasoning tasks
J Jo, V Verma, Y Bengio
arXiv preprint arXiv:1806.06765, 2018
Sketchtransfer: A new dataset for exploring detail-invariance and the abstractions learned by deep networks
A Lamb, S Ozair, V Verma, D Ha
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
MixupE: Understanding and improving Mixup from directional derivative perspective
Y Zou, V Verma, S Mittal, WH Tang, H Pham, J Kannala, Y Bengio, A Solin, ...
Uncertainty in Artificial Intelligence, 2597-2607, 2023
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