Meirav Galun

Computer Vision and Pattern Recognition · 2004–2021

A researcher in the Science corpus: 11 papers; trusts 377 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #5,567 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 84 citations into it; #31,639 of 35,345 by trust here, #25,040 by citations
• Computational Mechanics — 60 citations into it; #9,639 of 10,001 by trust here, #3,652 by citations
• Scaling & Learning Theory — 38 citations into it
• Computer Graphics and Computer-Aided Design — 37 citations into it
• Model Architectures — 26 citations into it; #1,933 of 2,278 by trust here, #1,402 by citations
• 3D Vision & Pose — 24 citations into it; #683 of 689 by trust here, #470 by citations
• Statistical Learning Theory — 23 citations into it

Whom Meirav Galun trusts overall

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

1. Jianbo Shi
2. Jitendra Malik
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Yann LeCun
7. Andrew Y. Ng
8. Yoshua Bengio
9. Yurii E. Nesterov
10. Diederik P. Kingma

Who trusts Meirav Galun overall

1. João Carreira
2. Cristian Sminchisescu
3. Pablo Arbeláez
4. Guanbin Li
5. Matthew Tancik
6. Nir Sharon
7. Ran Margolin
8. Huchuan Lu
9. Vida Movahedi
10. Xi Li

Papers from Meirav Galun's perspective

Ordered by how strongly the researchers Meirav Galun 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
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

• Convolutional neural networks on surfaces via seamless toric covers (2017) — 246 citations
• Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration (2007) — 621 citations
• Hierarchy and adaptivity in segmenting visual scenes (2006) — 302 citations
• Frequency Bias in Neural Networks for Input of Non-Uniform Density (2020) — 249 citations
• Fast multiscale clustering and manifold identification (2006) — 98 citations
• Efficient Multilevel Eigensolvers with Applications to Data Analysis Tasks (2010) — 31 citations
• Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks (2021) — 25 citations
• On the Similarity between the Laplace and Neural Tangent Kernels (2020) — 116 citations
• Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance (2020) — 997 citations
• Accelerated quadratic proxy for geometric optimization (2016) — 105 citations
• Shape representation and classification using the Poisson equation (2004) — 312 citations