Vinayak Tantia

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #22,102 of 35,345 by trust here, #19,018 by citations
• Optimization & Training — 46 citations into it; #1,847 of 2,601 by trust here, #2,052 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #13,726 of 21,575 by trust here, #15,955 by citations
• Computer Networks and Communications — 11 citations into it
• Model Architectures — 10 citations into it
• Federated & Distributed Learning — 8 citations into it
• Computational Mechanics — 7 citations into it
• ML Systems & Hardware — 7 citations into it; #337 of 575 by trust here, #464 by citations

Whom Vinayak Tantia trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Hao Yu
10. Jimmy Ba

Who trusts Vinayak Tantia overall

1. Jianyu Wang
2. Dmitrii Avdiukhin
3. Ahmed Khaled
4. Haibo Yang
5. Li Chen

Papers from Vinayak Tantia's perspective

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

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

Their papers

• A Modern Take on the Bias-Variance Tradeoff in Neural Networks (2018) — 186 citations
• SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum (2019) — 224 citations