Chandan Singh

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 287 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #14,143 of 35,345 by trust here, #8,439 by citations
• Safety, Robustness & Interpretability — 111 citations into it; #1,310 of 2,251 by trust here, #1,907 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #12,404 of 21,575 by trust here, #15,529 by citations
• Natural Language Processing — 49 citations into it
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Biophysics — 11 citations into it
• Safety Research — 9 citations into it

Whom Chandan Singh trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. W. James Murdoch
4. Alex Krizhevsky
5. Yoshua Bengio
6. Karen Simonyan
7. Diederik P. Kingma
8. Richard Socher
9. Matthew D. Zeiler
10. Kaiming He

Who trusts Chandan Singh overall

1. Xisen Jin
2. Michael Sejr Schlichtkrull
3. Dheeraj Rajagopal
4. Bin Yu
5. Nicola De Cao
6. David Vilar
7. W. James Murdoch
8. Wooseok Ha
9. Laura Rieger
10. Meike Nauta

Papers from Chandan Singh's perspective

Ordered by how strongly the researchers Chandan Singh 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. 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Hierarchical interpretations for neural network predictions (2018) — 162 citations
• Definitions, methods, and applications in interpretable machine learning (2019) — 1,811 citations
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting (2022) — 58 citations
• Transformation Importance with Applications to Cosmology (2020) — 11 citations
• Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (2019) — 258 citations