Chaitanya Ahuja

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 733 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #6,852 of 21,575 by trust here, #14,822 by citations
• Artificial Intelligence — 84 citations into it; #20,451 of 35,345 by trust here, #8,706 by citations
• Multimodal Learning — 70 citations into it; #682 of 1,211 by trust here, #526 by citations
• Natural Language Processing — 54 citations into it
• Signal Processing — 27 citations into it; #4,246 of 7,572 by trust here, #1,055 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Experimental and Cognitive Psychology — 15 citations into it
• Model Architectures — 12 citations into it

Whom Chaitanya Ahuja trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Alex Krizhevsky
5. Sepp Hochreiter
6. Diederik P. Kingma
7. Kaiming He
8. Karen Simonyan
9. Tomas Mikolov
10. Alex Graves

Who trusts Chaitanya Ahuja overall

1. Chuan Guo
2. Guy Tevet
3. Mathis Petrovich
4. Ye Yuan
5. Youngwoo Yoon
6. Xinxin Zuo
7. Xin Chen
8. Brian Gordon
9. Michael J. Black
10. Sen Wang

Papers from Chaitanya Ahuja's perspective

Ordered by how strongly the researchers Chaitanya Ahuja 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

• Multimodal Machine Learning: A Survey and Taxonomy (2017) — 4,410 citations
• Language2Pose: Natural Language Grounded Pose Forecasting (2019) — 380 citations