Kevin Zakka

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #20,805 of 21,575 by trust here, #13,542 by citations
• Reinforcement Learning — 25 citations into it
• Control and Systems Engineering — 20 citations into it; #8,218 of 8,257 by trust here, #5,227 by citations
• Aerospace Engineering — 12 citations into it; #6,391 of 6,421 by trust here, #4,570 by citations
• Representation & Self-Supervised Learning — 12 citations into it

Whom Kevin Zakka trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Sergey Levine
5. Alex Krizhevsky
6. Yoshua Bengio
7. Pieter Abbeel
8. Yann LeCun
9. Pierre Sermanet
10. L. V. D. van der Maaten

Who trusts Kevin Zakka overall

1. Andy Zeng
2. Chen Wang
3. Jimmy Wu
4. Shuran Song
5. Peter R. Florence

Papers from Kevin Zakka's perspective

Ordered by how strongly the researchers Kevin Zakka 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly (2019) — 123 citations
• XIRL: Cross-embodiment Inverse Reinforcement Learning (2021) — 160 citations