Vikas Verma

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 354 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it; #22,138 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 128 citations into it; #12,555 of 21,575 by trust here, #6,371 by citations
• Representation & Self-Supervised Learning — 71 citations into it; #1,915 of 3,145 by trust here, #767 by citations
• Optimization & Training — 51 citations into it
• Model Architectures — 26 citations into it
• Generative Models — 25 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Vikas Verma trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Christian Szegedy
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Ian J. Goodfellow
10. Sergey Ioffe

Who trusts Vikas Verma overall

1. Da-Wei Zhou
2. Geoff French
3. Christopher Beckham
4. Linjun Zhang
5. David Berthelot
6. Hongyu Guo
7. Jang-Hyun Kim
8. Minh-Thang Luong
9. Quoc V. Le
10. Sangdoo Yun

Papers from Vikas Verma's perspective

Ordered by how strongly the researchers Vikas Verma trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Interpolation Consistency Training for Semi-Supervised Learning (2019) — 919 citations
• Residual Connections Encourage Iterative Inference (2017) — 182 citations
• PatchUp: A Regularization Technique for Convolutional Neural Networks (2020) — 39 citations
• Towards Domain-Agnostic Contrastive Learning (2020) — 133 citations
• On Adversarial Mixup Resynthesis (2019) — 75 citations
• Manifold Mixup: Better Representations by Interpolating Hidden States (2018) — 1,613 citations
• Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer (2018) — 67 citations