Shubhendu Trivedi

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 7 papers; trusts 215 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #4,919 of 35,345 by trust here, #8,210 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #8,395 of 21,575 by trust here, #12,172 by citations
• Model Architectures — 36 citations into it; #329 of 2,278 by trust here, #625 by citations
• Graph Learning — 32 citations into it; #553 of 693 by trust here, #326 by citations
• Materials Chemistry — 30 citations into it
• Computational Chemistry & Drug Discovery — 29 citations into it
• Computational Theory and Mathematics — 25 citations into it; #1,137 of 12,182 by trust here, #1,748 by citations
• Atomic and Molecular Physics, and Optics — 12 citations into it

Whom Shubhendu Trivedi trusts overall

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

1. Risi Kondor
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Yoshua Bengio
7. Taco Cohen
8. Max Welling
9. Ilya Sutskever
10. Kaiming He

Who trusts Shubhendu Trivedi overall

1. Risi Kondor
2. David W. Romero
3. Taco Cohen
4. Maurice Weiler
5. Erik J. Bekkers
6. Brandon M. Anderson
7. Haggai Maron
8. Benjamin Bloem-Reddy
9. Alberto Bietti
10. Johannes Klicpera

Papers from Shubhendu Trivedi's perspective

Ordered by how strongly the researchers Shubhendu Trivedi 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups (2018) — 590 citations
• Covariant Compositional Networks For Learning Graphs (2018) — 168 citations
• Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (2018) — 315 citations
• Predicting molecular properties with covariant compositional networks. (2018) — 73 citations
• Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models (2023) — 322 citations
• DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks (2018) — 75 citations
• A Consistent Estimator of the Expected Gradient Outerproduct (2014) — 26 citations