Steven Euijong Whang
Artificial Intelligence · 2009–2026
A researcher in the Science corpus: 15 papers; trusts 353 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 278 citations into it; #15,450 of 35,345 by trust here, #9,760 by citations
- Management Science and Operations Research — 85 citations into it; #4,942 of 11,946 by trust here, #2,402 by citations
- Information Systems — 73 citations into it; #4,803 of 8,771 by trust here, #5,713 by citations
- Computer Vision and Pattern Recognition — 71 citations into it; #20,484 of 21,575 by trust here, #17,757 by citations
- Safety, Robustness & Interpretability — 53 citations into it; #1,804 of 2,251 by trust here, #2,161 by citations
- Computer Networks and Communications — 51 citations into it; #3,700 of 9,886 by trust here, #2,478 by citations
- Natural Language Processing — 48 citations into it
- Signal Processing — 42 citations into it; #7,099 of 7,572 by trust here, #3,913 by citations
Whom Steven Euijong Whang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ian J. Goodfellow
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Yann LeCun
- Ilya Sutskever
- Christian Szegedy
- Karen Simonyan
Who trusts Steven Euijong Whang overall
- Peter Christen
- Giorgio Patrini
- Christoph Böhm
- Yuji Roh
- Hanna Köpcke
- Gerard de Melo
- Erhard Rahm
- Richard Nock
- Felix Naumann
- Yasser Altowim
Papers from Steven Euijong Whang's perspective
Ordered by how strongly the researchers Steven Euijong Whang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017) — 478 citations
- ReNoun: Fact Extraction for Nominal Attributes (2014) — 104 citations
- Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach (2019) — 80 citations
- Goods: Organizing Google's Datasets (2016) — 213 citations
- Entity resolution with iterative blocking (2009) — 247 citations
- Joint Entity Resolution (2012) — 43 citations
- Data Lifecycle Challenges in Production Machine Learning (2018) — 234 citations
- Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval (2026)
- Data collection and quality challenges in deep learning: a data-centric AI perspective (2021) — 559 citations
- FairBatch: Batch Selection for Model Fairness (2020) — 163 citations
- Data Validation for Machine Learning (2019) — 369 citations
- A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018) — 815 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
- Biperpedia: An Ontology for Search Applications (2014) — 83 citations