Bill Freeman

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 683 researchers and is trusted by 2,015 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 233 citations into it; #892 of 21,575 by trust here, #1,289 by citations
• Computational Mechanics — 73 citations into it; #722 of 10,001 by trust here, #234 by citations
• Artificial Intelligence — 65 citations into it
• 3D Vision & Pose — 59 citations into it; #259 of 689 by trust here, #324 by citations
• Generative Models — 55 citations into it; #351 of 1,956 by trust here, #244 by citations
• Computer Graphics and Computer-Aided Design — 40 citations into it
• Aerospace Engineering — 35 citations into it
• Representation & Self-Supervised Learning — 20 citations into it

Whom Bill Freeman trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. David G. Lowe

Who trusts Bill Freeman overall

1. Jiajun Wu
2. Josh Tenenbaum
3. Andrea Vedaldi
4. Jitendra Malik
5. Jun-Yan Zhu
6. Julian Chibane
7. Georgia Gkioxari
8. Justin Johnson
9. Sergey Levine
10. Xian-Feng Han

Papers from Bill Freeman's perspective

Ordered by how strongly the researchers Bill Freeman 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016) — 2,154 citations
• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks (2016) — 418 citations
• Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (2015) — 409 citations
• Learning to See Physics via Visual De-animation (2017) — 228 citations
• MarrNet: 3D Shape Reconstruction via 2.5D Sketches (2017) — 440 citations
• Visual Object Networks: Image Generation with Disentangled 3D Representations (2018) — 273 citations
• Learning to Reconstruct Shapes from Unseen Classes (2018) — 164 citations
• The Event Horizon General Relativistic Magnetohydrodynamic Code Comparison Project (2019) — 278 citations