Chinmay Hegde

Computer Vision and Pattern Recognition · with Balu · 2008–2022

A researcher in the Science corpus: 4 papers; trusts 253 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #8,522 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 39 citations into it; #11,580 of 35,345 by trust here, #17,110 by citations
• Computational Mechanics — 36 citations into it; #1,078 of 10,001 by trust here, #1,006 by citations
• Optimization & Training — 20 citations into it
• Model Architectures — 16 citations into it
• Biomedical Engineering — 14 citations into it
• Computer Networks and Communications — 14 citations into it; #2,447 of 9,886 by trust here, #3,638 by citations
• Signal Processing — 13 citations into it

Whom Chinmay Hegde trusts overall

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

1. E.J. Candes
2. Yann LeCun
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. David L. Donoho
7. Alex Krizhevsky
8. Kaiming He
9. L. Eon Bottou
10. Michael Elad

Who trusts Chinmay Hegde overall

1. Francis R. Bach
2. Jianyu Wang
3. Hao Yu
4. Ajil Jalal
5. Marco F. Duarte
6. Rodolphe Jenatton
7. Ankit Raj
8. Christopher A. Metzler
9. Xiang Li
10. Rushil Anirudh

Papers from Chinmay Hegde's perspective

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

Their papers

• Solving Linear Inverse Problems Using Gan Priors: An Algorithm with Provable Guarantees (2018) — 183 citations
• Collaborative Deep Learning in Fixed Topology Networks (2017) — 201 citations
• On The Computational Complexity of Self-Attention (2022) — 298 citations
• Model-Based Compressive Sensing (2008) — 2,612 citations