Mert Gurbuzbalaban

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 5 papers; trusts 64 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #11,046 of 35,345 by trust here, #12,580 by citations
• Optimization & Training — 78 citations into it; #2,225 of 2,601 by trust here, #1,866 by citations
• Computational Mechanics — 55 citations into it; #3,374 of 10,001 by trust here, #3,559 by citations
• Computer Networks and Communications — 24 citations into it
• Statistics and Probability — 17 citations into it; #10,307 of 12,932 by trust here, #5,857 by citations
• Numerical Analysis — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Management Science and Operations Research — 12 citations into it

Whom Mert Gurbuzbalaban trusts overall

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

1. Yurii E. Nesterov
2. Dimitri P. Bertsekas
3. Paul Tseng
4. Francis R. Bach
5. Aaron Defazio
6. Mark W. Schmidt
7. Nicolas Le Roux
8. L. Eon Bottou
9. M. Solodov
10. Guanghui Lan

Who trusts Mert Gurbuzbalaban overall

1. Nicolas Loizou
2. Aryan Mokhtari
3. Angelia Nedić
4. Guanghui Lan
5. Kwangjun Ahn
6. Lam M. Nguyen
7. Baharan Mirzasoleiman
8. Jakub Konecný
9. Alexander Olshevsky
10. Stephanie L. Hyland

Papers from Mert Gurbuzbalaban's perspective

Ordered by how strongly the researchers Mert Gurbuzbalaban 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
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

• Why random reshuffling beats stochastic gradient descent (2015) — 181 citations
• A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks (2019) — 354 citations
• Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances (2019) — 49 citations
• Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks (2019) — 33 citations
• On the Convergence Rate of Incremental Aggregated Gradient Algorithms (2015) — 151 citations