Vukosi Marivate

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 4 papers; trusts 154 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #15,685 of 35,345 by trust here, #29,412 by citations
• Reinforcement Learning — 40 citations into it
• Natural Language Processing — 21 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Management Science and Operations Research — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it; #5,059 of 12,182 by trust here, #10,349 by citations
• Statistics and Probability — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Vukosi Marivate trusts overall

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

1. Richard S. Sutton
2. Leemon C. Baird III
3. Justin A. Boyan
4. Geoffrey E. Hinton
5. Ron Kohavi
6. Richard Bellman
7. Tim Hesterberg
8. Andrew Barto
9. Robi Polikar
10. Andrew Y. Ng

Who trusts Vukosi Marivate overall

1. Xiujun Li
2. Taylor W. Killian
3. Kavosh Asadi
4. Michael L. Littman
5. Lihong Li
6. Chao Yu
7. Nan Jiang
8. George Dimitri Konidaris
9. Saurabh Arora
10. Zhifei Shao

Papers from Vukosi Marivate's perspective

Ordered by how strongly the researchers Vukosi Marivate 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. Attention is All you Need (Neural Information Processing Systems)
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

• Improved empirical methods in reinforcement-learning evaluation (2015) — 10 citations
• Improving short text classification through global augmentation methods (2019) — 111 citations
• Quantifying Uncertainty in Batch Personalized Sequential Decision Making (2014) — 14 citations
• Apprenticeship Learning About Multiple Intentions (2011) — 197 citations