Aaron Walsman

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 300 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 115 citations into it; #16,807 of 21,575 by trust here, #7,421 by citations
• Artificial Intelligence — 91 citations into it
• Representation & Self-Supervised Learning — 44 citations into it
• Aerospace Engineering — 28 citations into it
• Evaluation & Benchmarks — 26 citations into it; #2,313 of 3,147 by trust here, #1,817 by citations
• Robotics & Control — 20 citations into it
• Reinforcement Learning — 19 citations into it
• Control and Systems Engineering — 15 citations into it; #3,970 of 8,257 by trust here, #759 by citations

Whom Aaron Walsman trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ross B. Girshick
8. Rosen Diankov
9. Morgan Quigley
10. Ilya Sutskever

Who trusts Aaron Walsman overall

1. Douglas Morrison
2. Joshua Tobin
3. Yu Xiang
4. Adithyavairavan Murali
5. Peter Corke
6. Huy Ha

Papers from Aaron Walsman's perspective

Ordered by how strongly the researchers Aaron Walsman 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• CHALET: Cornell House Agent Learning Environment (2018) — 101 citations
• Yale-CMU-Berkeley dataset for robotic manipulation research (2017) — 453 citations
• Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015) — 600 citations
• Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015) — 326 citations
• Are We Overfitting to Experimental Setups in Recognition (2020) — 6 citations
• In the Wild: From ML Models to Pragmatic ML Systems (2020) — 9 citations
• The YCB object and Model set: Towards common benchmarks for manipulation research (2015) — 1,019 citations