Ajay Jain

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 683 researchers and is trusted by 2,647 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 205 citations into it; #751 of 21,575 by trust here, #819 by citations
• Artificial Intelligence — 134 citations into it; #724 of 35,345 by trust here, #1,296 by citations
• Generative Models — 106 citations into it; #58 of 1,956 by trust here, #100 by citations
• Computer Graphics and Computer-Aided Design — 46 citations into it; #161 of 1,584 by trust here, #126 by citations
• Natural Language Processing — 39 citations into it
• Representation & Self-Supervised Learning — 39 citations into it
• Computational Mechanics — 37 citations into it; #8,495 of 10,001 by trust here, #2,509 by citations
• Model Architectures — 34 citations into it

Whom Ajay Jain trusts overall

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

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

Who trusts Ajay Jain overall

1. Yang Song
2. Alex Nichol
3. Heewoo Jun
4. Jonathan Ho
5. Arash Vahdat
6. Prafulla Dhariwal
7. Chenlin Meng
8. Jiawei Yang
9. Diederik P. Kingma
10. Yilun Du

Papers from Ajay Jain's perspective

Ordered by how strongly the researchers Ajay Jain 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Denoising Diffusion Probabilistic Models (2020) — 33,172 citations
• DreamFusion: Text-to-3D using 2D Diffusion (2022) — 3,724 citations
• Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis (2021) — 640 citations
• Zero-Shot Text-Guided Object Generation with Dream Fields (2021) — 672 citations
• Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization (2019) — 244 citations
• Contrastive Code Representation Learning (2020) — 179 citations