Jayaraman J. Thiagarajan

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 191 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #15,042 of 21,575 by trust here, #16,367 by citations
• Artificial Intelligence — 43 citations into it; #24,193 of 35,345 by trust here, #16,771 by citations
• Generative Models — 27 citations into it
• Safety, Robustness & Interpretability — 14 citations into it; #1,586 of 2,251 by trust here, #2,161 by citations
• Natural Language Processing — 10 citations into it
• Model Architectures — 7 citations into it

Whom Jayaraman J. Thiagarajan trusts overall

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

1. Ian J. Goodfellow
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Christian Szegedy

Who trusts Jayaraman J. Thiagarajan overall

1. Giulia Vilone
2. Amina Adadi
3. Vanessa Buhrmester
4. Shusen Liu
5. Riccardo Guidotti
6. Longo Luca
7. Mohammed Berrada
8. Alejandro Barredo Arrieta
9. David Münch
10. Bhavya Kailkhura

Papers from Jayaraman J. Thiagarajan's perspective

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

Their papers

• MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking (2019) — 47 citations
• Attend and Diagnose: Clinical Time Series Analysis using Attention Models (2017) — 530 citations
• TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning (2016) — 44 citations