Samet Oymak

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 424 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it; #8,934 of 35,345 by trust here, #12,244 by citations
• Optimization & Training — 122 citations into it; #961 of 2,601 by trust here, #1,195 by citations
• Computer Vision and Pattern Recognition — 105 citations into it
• Scaling & Learning Theory — 94 citations into it; #142 of 428 by trust here, #300 by citations
• Statistical Learning Theory — 82 citations into it; #343 of 1,038 by trust here, #405 by citations
• Computational Mechanics — 62 citations into it; #2,904 of 10,001 by trust here, #4,039 by citations
• Statistics and Probability — 43 citations into it
• Statistical and Nonlinear Physics — 27 citations into it; #9,919 of 10,127 by trust here, #7,024 by citations

Whom Samet Oymak trusts overall

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

1. Peter L. Bartlett
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Alex Graves
8. M. Soltanolkotabi
9. B. Recht
10. Simon Shaolei Du

Who trusts Samet Oymak overall

1. Reinhard Heckel
2. M. Soltanolkotabi
3. Zeyuan Allen-Zhu
4. Atsushi Nitanda
5. Mingchen Li
6. Yuanzhi Li
7. Difan Zou
8. Taiji Suzuki
9. Guodong Zhang
10. Navid Azizan

Papers from Samet Oymak's perspective

Ordered by how strongly the researchers Samet Oymak 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian (2019) — 108 citations
• Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? (2018) — 200 citations
• Provable and Efficient Continual Representation Learning (2022) — 16 citations
• Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks (2019) — 396 citations
• Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks (2019) — 349 citations
• Learning Compact Neural Networks with Regularization (2018) — 39 citations
• Non-asymptotic Identification of LTI Systems from a Single Trajectory (2018) — 252 citations