Vivek Veeriah

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 150 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #8,614 of 35,345 by trust here, #8,999 by citations
• Reinforcement Learning — 60 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it; #5,966 of 21,575 by trust here, #7,852 by citations
• Optimization & Training — 15 citations into it
• Management Science and Operations Research — 13 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Meta & Continual Learning — 11 citations into it
• Model Architectures — 10 citations into it; #696 of 2,278 by trust here, #1,092 by citations

Whom Vivek Veeriah trusts overall

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

1. Richard S. Sutton
2. Alex Graves
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. J. Urgen Schmidhuber
7. Sepp Hochreiter
8. Yann LeCun
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Vivek Veeriah overall

1. Pichao Wang
2. Jun Liu
3. Qiuhong Ke
4. Guillermo Garcia-Hernando
5. Wanqing Li
6. Hongsong Wang
7. Amir Shahroudy
8. Mohammed Bennamoun
9. Diogo Carbonera Luvizon
10. Ke Cheng

Papers from Vivek Veeriah's perspective

Ordered by how strongly the researchers Vivek Veeriah 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Many-Goals Reinforcement Learning (2018) — 56 citations
• Discovery of Useful Questions as Auxiliary Tasks (2019) — 90 citations
• Self-Tuning Deep Reinforcement Learning (2020) — 15 citations
• Differential Recurrent Neural Networks for Action Recognition (2015) — 492 citations
• Deep Learning Architecture with Dynamically Programmed Layers for Brain Connectome Prediction (2015) — 13 citations