Surbhi Goel

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #19,034 of 35,345 by trust here, #19,873 by citations
• Statistical Learning Theory — 24 citations into it; #492 of 1,038 by trust here, #583 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 18 citations into it
• Model Architectures — 12 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Algorithms & Complexity — 6 citations into it
• Scaling & Learning Theory — 6 citations into it

Whom Surbhi Goel trusts overall

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

1. Adam R. Klivans
2. Amit Daniely
3. Shai Shalev-Shwartz
4. Peter L. Bartlett
5. Adam Tauman Kalai
6. Leslie G. Valiant
7. Christopher K. I. Williams
8. Sham M. Kakade
9. Vitaly Feldman
10. Jaume Amores

Who trusts Surbhi Goel overall

1. Zeyuan Allen-Zhu
2. Rong Ge
3. M. Soltanolkotabi
4. Yuanzhi Li
5. Rohith Kuditipudi
6. Simon Shaolei Du
7. Pasin Manurangsi
8. Zhize Li
9. Adam R. Klivans
10. Ainesh Bakshi

Papers from Surbhi Goel's perspective

Ordered by how strongly the researchers Surbhi Goel 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Reliably Learning the ReLU in Polynomial Time (2016) — 131 citations
• Learning One Convolutional Layer with Overlapping Patches (2018) — 81 citations
• Learning Neural Networks with Two Nonlinear Layers in Polynomial Time (2017) — 78 citations