Eldad Haber

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 12 papers; trusts 351 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #5,935 of 21,575 by trust here, #6,795 by citations
• Artificial Intelligence — 91 citations into it; #6,313 of 35,345 by trust here, #8,619 by citations
• Optimization & Training — 38 citations into it; #960 of 2,601 by trust here, #1,365 by citations
• Model Architectures — 37 citations into it; #391 of 2,278 by trust here, #694 by citations
• Generative Models — 27 citations into it
• Statistical and Nonlinear Physics — 17 citations into it; #755 of 10,127 by trust here, #1,100 by citations
• Computational Mechanics — 16 citations into it; #3,419 of 10,001 by trust here, #3,463 by citations
• Signal Processing — 16 citations into it

Whom Eldad Haber trusts overall

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

1. Uri M. Ascher
2. Kaiming He
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Jorge Nocedal
9. L. Eon Bottou
10. X. Zhang

Who trusts Eldad Haber overall

1. Martin Burger
2. Uri M. Ascher
3. Michele Benzi
4. Joshua D. Deaton
5. Yiping Lu
6. Zhen Luo
7. Xuechen Li
8. Jens Behrmann
9. Lars Ruthotto
10. Zichao Long

Papers from Eldad Haber's perspective

Ordered by how strongly the researchers Eldad Haber 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. 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

• Reversible Architectures for Arbitrarily Deep Residual Neural Networks (2017) — 298 citations
• Stable architectures for deep neural networks (2017) — 856 citations
• Multi-level Residual Networks from Dynamical Systems View (2017) — 184 citations
• Deep Neural Networks Motivated by Partial Differential Equations (2018) — 570 citations
• Fast Simulation of 3D Electromagnetic Problems Using Potentials (2000) — 232 citations
• An introduction to deep generative modeling (2021) — 305 citations
• 3-D inversion of airborne electromagnetic data parallelized and accelerated by local mesh and adaptive soundings (2014) — 146 citations
• RESINVM3D: A 3D resistivity inversion package (2007) — 218 citations
• Intensity Gradient Based Registration and Fusion of Multi-modal Images (2006) — 400 citations
• A multilevel, level-set method for optimizing eigenvalues in shape design problems (2004) — 82 citations
• Inversion of 3D electromagnetic data in frequency and time domain using an inexact all-at-once approach (2004) — 192 citations
• On optimization techniques for solving nonlinear inverse problems (2000) — 346 citations