Jacob Walker

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 7 papers; trusts 931 researchers and is trusted by 1,437 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 231 citations into it; #1,082 of 21,575 by trust here, #2,768 by citations
• Artificial Intelligence — 114 citations into it; #3,659 of 35,345 by trust here, #4,682 by citations
• Generative Models — 71 citations into it; #437 of 1,956 by trust here, #448 by citations
• Representation & Self-Supervised Learning — 38 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Reinforcement Learning — 19 citations into it
• Model Architectures — 18 citations into it
• Optimization & Training — 12 citations into it

Whom Jacob Walker trusts overall

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

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

Who trusts Jacob Walker overall

1. Chelsea Finn
2. Abhinav Kumar Gupta
3. Chen Sun
4. Roozbeh Mottaghi
5. Sergey Levine
6. Carl Vondrick
7. De-An Huang
8. Jiajun Wu
9. Carl Doersch
10. Kris M. Kitani

Papers from Jacob Walker's perspective

Ordered by how strongly the researchers Jacob Walker 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. 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. Going deeper with convolutions

Their papers

• An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders (2016) — 526 citations
• Patch to the Future: Unsupervised Visual Prediction (2014) — 207 citations
• Dense Optical Flow Prediction from a Static Image (2015) — 220 citations
• The Pose Knows: Video Forecasting by Generating Pose Futures (2017) — 359 citations
• Transframer: Arbitrary Frame Prediction with Generative Models (2022) — 47 citations
• Predicting Video with VQVAE (2021) — 81 citations
• Representation Learning via Invariant Causal Mechanisms (2020) — 290 citations