Murat A. Erdogdu
Artificial Intelligence · 2015–2021
A researcher in the Science corpus: 6 papers; trusts 76 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 82 citations into it; #5,580 of 35,345 by trust here, #15,542 by citations
- Optimization & Training — 74 citations into it; #570 of 2,601 by trust here, #1,537 by citations
- Computational Mechanics — 38 citations into it; #1,099 of 10,001 by trust here, #3,763 by citations
- Statistics and Probability — 32 citations into it
- Scaling & Learning Theory — 30 citations into it
- Statistical Learning Theory — 29 citations into it
- Computer Vision and Pattern Recognition — 26 citations into it
- Numerical Analysis — 10 citations into it
Whom Murat A. Erdogdu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- L. Eon Bottou
- Richard H. Byrd
- Nicolas Le Roux
- David F. Shanno
- Donald Goldfarb
- C. G. Broyden
- Olivier Chapelle
- Shun-ichi Amari
- Shai Shalev-Shwartz
Who trusts Murat A. Erdogdu overall
- Farbod Roosta-Khorasani
- Raghu Bollapragada
- Aryan Mokhtari
- Daniel Jiwoong Im
- S. Meng
- Frank E. Curtis
- Siyuan Ma
- Naman Agarwal
- Albert S. Berahas
- Mark Eisen
Papers from Murat A. Erdogdu's perspective
Ordered by how strongly the researchers Murat A. Erdogdu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Convergence rates of sub-sampled Newton methods (2015) — 167 citations
- Manipulating SGD with Data Ordering Attacks (2021) — 122 citations
- Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint (2020) — 78 citations
- Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma (2015) — 13 citations
- SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015) — 704 citations
- Accelerating SVRG via second-order information (2015)