Pankajan Chanthirasegaran

Artificial Intelligence · 2014–2016

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #22,964 of 35,345 by trust here, #23,077 by citations
• Natural Language Processing — 22 citations into it
• Information Systems — 20 citations into it; #5,044 of 8,771 by trust here, #5,078 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Model Architectures — 6 citations into it
• Signal Processing — 5 citations into it

Whom Pankajan Chanthirasegaran trusts overall

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

1. Richard Socher
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Yoshua Bengio
6. Jimmy Ba
7. Andrew Y. Ng
8. Wojciech Zaremba
9. David M. Blei
10. Alex Graves

Who trusts Pankajan Chanthirasegaran overall

1. David Saxton
2. Xinyun Chen
3. Diane Bouchacourt
4. Forough Arabshahi
5. Richard Evans

Papers from Pankajan Chanthirasegaran's perspective

Ordered by how strongly the researchers Pankajan Chanthirasegaran 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Continuous Semantic Representations of Symbolic Expressions (2016) — 110 citations
• Autofolding for Source Code Summarization (2014) — 57 citations