Ravid Shwartz-Ziv

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #3,751 of 35,345 by trust here, #5,533 by citations
• Evaluation & Benchmarks — 25 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #3,638 of 21,575 by trust here, #4,469 by citations
• Optimization & Training — 12 citations into it
• Model Architectures — 11 citations into it
• Natural Language Processing — 10 citations into it
• Signal Processing — 7 citations into it
• Information Theory — 5 citations into it

Whom Ravid Shwartz-Ziv trusts overall

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

1. Leo Breiman
2. Jerome H. Friedman
3. Simon S. Haykin
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Liam Paninski
7. Yann LeCun
8. Kaiming He
9. Naftali Tishby
10. Alex Krizhevsky

Who trusts Ravid Shwartz-Ziv overall

1. Alessandro Achille
2. Jacob Andreas
3. Frederik Benzing
4. Naomi Saphra
5. Luis A. Lastras
6. Nicholas Carlini
7. Charles H. Martin
8. Rana Ali Amjad
9. Erico Tjoa
10. Stefano Soatto

Papers from Ravid Shwartz-Ziv's perspective

Ordered by how strongly the researchers Ravid Shwartz-Ziv 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Opening the Black Box of Deep Neural Networks via Information (2017) — 1,641 citations
• LiveBench: A Challenging, Contamination-Free LLM Benchmark (2024) — 317 citations
• Tabular Data: Deep Learning is Not All You Need (2021) — 2,138 citations