Kailas Vodrahalli

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 47 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Scaling & Learning Theory — 9 citations into it
• Statistics and Probability — 9 citations into it
• Computational Mechanics — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Kailas Vodrahalli trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Karen Simonyan
4. Geoffrey E. Hinton
5. Jia Deng
6. Kaiming He
7. Andrew Zisserman
8. Diederik P. Kingma
9. Vladimir Naumovich Vapnik
10. Yoshua Bengio

Who trusts Kailas Vodrahalli overall

1. Pengzhen Ren
2. Swabha Swayamdipta
3. Kawin Ethayarajh
4. Peter L. Bartlett
5. Pavel Ostyakov
6. Vidya Muthukumar
7. Yun Xiao

Papers from Kailas Vodrahalli's perspective

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

Their papers

• Are All Training Examples Created Equal? An Empirical Study (2018) — 65 citations
• Harmless interpolation of noisy data in regression (2019) — 213 citations
• Can large language models provide useful feedback on research papers? A large-scale empirical analysis (2023) — 341 citations
• Disparities in dermatology AI performance on a diverse, curated clinical image set (2021) — 463 citations