Jacob Menick

Computer Vision and Pattern Recognition · with Battaglia · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 146 researchers and is trusted by 171 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #2,778 of 21,575 by trust here, #8,021 by citations
• Generative Models — 31 citations into it; #258 of 1,956 by trust here, #730 by citations
• Artificial Intelligence — 28 citations into it
• Model Architectures — 12 citations into it
• Signal Processing — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Efficiency & Compression — 5 citations into it
• Natural Language Processing — 5 citations into it

Whom Jacob Menick trusts overall

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

1. Aäron van den Oord
2. Diederik P. Kingma
3. Nal Kalchbrenner
4. Geoffrey E. Hinton
5. Tero Karras
6. Alex Krizhevsky
7. Yoshua Bengio
8. Lars M. Mescheder
9. Kaiming He
10. Alex Graves

Who trusts Jacob Menick overall

1. Rewon Child
2. Jonathan Ho
3. Chitwan Saharia
4. Dirk Weissenborn
5. Charlie Nash
6. Xuezhe Ma
7. Sean Vasquez
8. Jacob Walker
9. Pieter Abbeel
10. William Chan

Papers from Jacob Menick's perspective

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

Their papers

• Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling (2018) — 158 citations
• Generating Images with Sparse Representations (2021) — 321 citations