Suriya Gunasekar

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 10 papers; trusts 57 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #7,875 of 35,345 by trust here, #4,956 by citations
• Optimization & Training — 55 citations into it; #484 of 2,601 by trust here, #373 by citations
• Scaling & Learning Theory — 44 citations into it; #105 of 428 by trust here, #92 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Computational Mechanics — 41 citations into it; #1,096 of 10,001 by trust here, #636 by citations
• Safety, Robustness & Interpretability — 29 citations into it
• Statistical Learning Theory — 17 citations into it
• Safety Research — 13 citations into it

Whom Suriya Gunasekar trusts overall

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

1. Behnam Neyshabur
2. Nati Srebro
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yoshua Bengio
7. B. Recht
8. Sepp Hochreiter
9. Ruslan Salakhutdinov
10. Yann LeCun

Who trusts Suriya Gunasekar overall

1. Colin Wei
2. Jason D. Lee
3. Daniel Soudry
4. Tengyu Ma
5. Yuanzhi Li
6. Sanjeev Arora
7. Navid Azizan
8. Blake E. Woodworth
9. Yuan Cao
10. Kaifeng Lyu

Papers from Suriya Gunasekar's perspective

Ordered by how strongly the researchers Suriya Gunasekar 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Implicit Bias of Gradient Descent on Linear Convolutional Networks (2018) — 483 citations
• Characterizing Implicit Bias in Terms of Optimization Geometry (2018) — 476 citations
• Textbooks Are All You Need (2023) — 626 citations
• Kernel and Rich Regimes in Overparametrized Models (2019) — 437 citations
• Textbooks Are All You Need II: phi-1.5 technical report (2023) — 664 citations
• Kernel and Deep Regimes in Overparametrized Models (2019) — 33 citations
• On preserving non-discrimination when combining expert advice (2018) — 31 citations
• The Implicit Bias of Gradient Descent on Separable Data (2017) — 1,145 citations
• Implicit Regularization in Matrix Factorization (2017) — 585 citations
• Learning Non-Discriminatory Predictors (2017) — 404 citations