Zohar S. Karnin

Artificial Intelligence · 2012–2022

A researcher in the Science corpus: 7 papers; trusts 63 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #10,092 of 35,345 by trust here, #14,181 by citations
• Management Science and Operations Research — 28 citations into it; #2,976 of 11,946 by trust here, #4,602 by citations
• Natural Language Processing — 28 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it; #15,045 of 21,575 by trust here, #15,162 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Optimization & Training — 18 citations into it
• Reinforcement Learning — 16 citations into it; #535 of 2,242 by trust here, #952 by citations
• Computer Networks and Communications — 15 citations into it

Whom Zohar S. Karnin trusts overall

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

1. Peter Auer
2. Sébastien Bubeck
3. Thorsten Joachims
4. Yisong Yue
5. Eyal Even-Dar
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Shie Mannor
9. R´emi Munos
10. Diederik P. Kingma

Who trusts Zohar S. Karnin overall

1. Kevin G. Jamieson
2. Aadirupa Saha
3. Lisha Li
4. Tor Lattimore
5. Liam Li
6. Li Yang
7. Ameet Talwalkar
8. Aleksandrs Slivkins
9. Yimin Huang
10. Afshin Rostamizadeh

Papers from Zohar S. Karnin's perspective

Ordered by how strongly the researchers Zohar S. Karnin 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Reducing Dueling Bandits to Cardinal Bandits (2014) — 157 citations
• Improving language models fine-tuning with representation consistency targets (2022)
• Amazon SageMaker Autopilot: a white box AutoML solution at scale (2020) — 77 citations
• TabTransformer: Tabular Data Modeling Using Contextual Embeddings (2020) — 875 citations
• Copeland Dueling Bandits (2015) — 100 citations
• Almost Optimal Exploration in Multi-Armed Bandits (2013) — 557 citations
• Annual Conference Computational Learning Theory: Preface (2012) — 439 citations