Brody Huval

Computer Vision and Pattern Recognition · 2012–2015

A researcher in the Science corpus: 4 papers; trusts 111 researchers and is trusted by 118 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it
• Artificial Intelligence — 31 citations into it
• Natural Language Processing — 31 citations into it; #609 of 5,688 by trust here, #981 by citations
• Representation & Self-Supervised Learning — 22 citations into it; #456 of 3,145 by trust here, #554 by citations
• Aerospace Engineering — 10 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 7 citations into it; #1,691 of 2,601 by trust here, #626 by citations

Whom Brody Huval trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Richard Socher
4. Yann LeCun
5. Yoshua Bengio
6. Andrew Y. Ng
7. Ilya Sutskever
8. Christian Szegedy
9. Kaiming He
10. Ross B. Girshick

Who trusts Brody Huval overall

1. Richard Socher
2. Karl Moritz Hermann
3. Yoshua Bengio
4. Phil Blunsom
5. Yoav Goldberg
6. Edward Grefenstette
7. Christopher D. Manning
8. Ryan Kiros
9. Jianfeng Gao
10. Ruslan Salakhutdinov

Papers from Brody Huval's perspective

Ordered by how strongly the researchers Brody Huval 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• An Empirical Evaluation of Deep Learning on Highway Driving (2015) — 625 citations
• Deep learning with COTS HPC systems (2013) — 755 citations
• Semantic Compositionality through Recursive Matrix-Vector Spaces (2012) — 1,414 citations
• Convolutional-Recursive Deep Learning for 3D Object Classification (2012) — 648 citations