Vignesh Jagadeesh

Computer Vision and Pattern Recognition · 2014

A researcher in the Science corpus: 5 papers; trusts 566 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 112 citations into it; #14,516 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 47 citations into it; #29,982 of 35,345 by trust here, #12,083 by citations
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Media Technology — 7 citations into it
• Model Architectures — 6 citations into it
• Molecular Biology — 5 citations into it
• Multimodal Learning — 5 citations into it

Whom Vignesh Jagadeesh trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Andrew Zisserman
5. David G. Lowe
6. Ruslan Salakhutdinov
7. Kaiming He
8. Yoshua Bengio
9. Jia Deng
10. Yann LeCun

Who trusts Vignesh Jagadeesh overall

1. Ruining He
2. Julian McAuley
3. Junshi Huang
4. Yujie Lin
5. Karim Ahmed
6. Yanming Guo
7. Rogério Schmidt Feris

Papers from Vignesh Jagadeesh's perspective

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

Their papers

• HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition (2014) — 483 citations
• ConceptLearner: Discovering visual concepts from weakly labeled image collections (2014) — 48 citations
• HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification (2014) — 65 citations
• Large scale visual recommendations from street fashion images (2014) — 143 citations
• Furniture-geek: Understanding fine-grained furniture attributes from freely associated text and tags (2014) — 10 citations