Gilad Baruch

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 36 citations into it
• Artificial Intelligence — 20 citations into it; #20,012 of 35,345 by trust here, #18,186 by citations
• Evaluation & Benchmarks — 11 citations into it
• Optimization & Training — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Federated & Distributed Learning — 7 citations into it; #175 of 319 by trust here, #237 by citations

Whom Gilad Baruch trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Daniel Scharstein
4. Geoffrey E. Hinton
5. Mu Li
6. Kaiming He
7. John C. Duchi
8. Sergey Zagoruyko
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Gilad Baruch overall

1. Virat Shejwalkar
2. Chien-Lun Chen
3. Shashank Rajput
4. Zaixi Zhang
5. Hong Chang

Papers from Gilad Baruch's perspective

Ordered by how strongly the researchers Gilad Baruch 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. ImageNet: A large-scale hierarchical image database
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
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

• A Little Is Enough: Circumventing Defenses For Distributed Learning (2019) — 732 citations
• ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data (2021) — 470 citations