Mathew Monfort

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 101 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #13,248 of 21,575 by trust here, #1,712 by citations
• Artificial Intelligence — 39 citations into it; #27,225 of 35,345 by trust here, #2,723 by citations
• Automotive Engineering — 10 citations into it; #2,088 of 3,081 by trust here, #201 by citations

Whom Mathew Monfort trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Andrew Zisserman
6. Bolei Zhou
7. Ivan Laptev
8. Christian Szegedy
9. Sergey Ioffe
10. Ilya Sutskever

Who trusts Mathew Monfort overall

1. A. Piergiovanni
2. Longlong Jing
3. Kshitij Dwivedi
4. Jiahui Yu
5. Anurag Arnab
6. Dian Shao
7. Lore Goetschalckx
8. Mostafa Dehghani
9. Mahesh Subedar
10. Farzaneh Mahdisoltani

Papers from Mathew Monfort's perspective

Ordered by how strongly the researchers Mathew Monfort 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• End to End Learning for Self-Driving Cars (2016) — 4,658 citations
• Moments in Time Dataset: One Million Videos for Event Understanding (2018) — 632 citations
• Multi-Agent Tensor Fusion for Contextual Trajectory Prediction (2019) — 465 citations