Neoklis Polyzotis

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 11 papers; trusts 13 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #26,058 of 35,345 by trust here, #8,706 by citations
• Management Science and Operations Research — 28 citations into it; #7,443 of 11,946 by trust here, #2,241 by citations
• Information Systems — 27 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #16,510 of 21,575 by trust here, #9,435 by citations
• Statistics and Probability — 16 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Neoklis Polyzotis trusts overall

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

1. Yoshua Bengio
2. Alison L. Gibbs
3. Minsuk Kahng
4. Songyun Duan
5. Alexander J. Ratner
6. Sanjay Krishnan
7. Geoffrey E. Hinton
8. Manasi Vartak
9. Alex Graves
10. Zheguang Zhao

Who trusts Neoklis Polyzotis overall

1. Yuji Roh
2. Steven Euijong Whang
3. Ki Hyun Tae
4. Nithya Sambasivan
5. Lukas Budach
6. Jie M. Zhang
7. Hwanjun Song
8. Geon Heo
9. Adam Marcus

Papers from Neoklis Polyzotis's perspective

Ordered by how strongly the researchers Neoklis Polyzotis 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Et al
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017) — 478 citations
• The Case for Learned Index Structures (2017) — 1,261 citations
• Goods: Organizing Google's Datasets (2016) — 213 citations
• CrowdScreen: algorithms for filtering data with humans (2012) — 250 citations
• Deco: A System for Declarative Crowdsourcing (2012) — 92 citations
• Data Lifecycle Challenges in Production Machine Learning (2018) — 234 citations
• Data Validation for Machine Learning (2019) — 369 citations
• Data Management Challenges in Production Machine Learning (2017) — 243 citations
• Managing Google's data lake: an overview of the Goods system (2016) — 67 citations
• SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics (2015) — 318 citations
• Asking the Right Questions in Crowd Data Sourcing (2012) — 85 citations