S. D. Babacan

Computational Mechanics · 2010–2013

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 36 citations into it; #4,714 of 10,001 by trust here, #5,813 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Signal Processing — 17 citations into it; #3,578 of 7,572 by trust here, #4,560 by citations
• Statistics and Probability — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Biomedical Engineering — 6 citations into it; #4,353 of 10,734 by trust here, #9,539 by citations

Whom S. D. Babacan trusts overall

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

1. Radford M. Neal
2. David L. Donoho
3. Michael V. Boland
4. E.J. Candes
5. Ruslan Salakhutdinov
6. Matthew J. Beal
7. Zhouchen Lin
8. George Eastman House
9. Yaakov Tsaig
10. Kiryung Lee

Who trusts S. D. Babacan overall

1. Thierry Bouwmans
2. Zai Yang
3. Qi Xie
4. Andrea Massa
5. Yale Song
6. Qibin Zhao
7. Shuhang Gu
8. Lihua Xie
9. Paolo Rocca
10. Deyu Meng

Papers from S. D. Babacan's perspective

Ordered by how strongly the researchers S. D. Babacan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Bayesian Compressive Sensing Using Laplace Priors (2010) — 798 citations
• Global analytic solution of fully-observed variational Bayesian matrix factorization (2013) — 158 citations
• Sparse Bayesian Methods for Low-Rank Matrix Estimation (2011) — 304 citations