Henry R. Ehrenberg
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 58 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #8,371 of 35,345 by trust here, #5,390 by citations
- Computer Vision and Pattern Recognition — 42 citations into it; #11,538 of 21,575 by trust here, #7,176 by citations
- Natural Language Processing — 37 citations into it
- Evaluation & Benchmarks — 30 citations into it; #1,689 of 3,147 by trust here, #1,399 by citations
- Representation & Self-Supervised Learning — 24 citations into it; #1,307 of 3,145 by trust here, #848 by citations
- Management Science and Operations Research — 16 citations into it
- Signal Processing — 12 citations into it
- Generative Models — 10 citations into it
Whom Henry R. Ehrenberg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yann LeCun
- Alex Krizhevsky
- Yoshua Bengio
- Alexander J. Ratner
- Ilya Sutskever
- Jimmy Ba
- X. Zhang
Who trusts Henry R. Ehrenberg overall
- Ekin Dogus Cubuk
- Alexander J. Ratner
- Barret Zoph
- Braden Hancock
- Christopher R'e
- Raphael Gontijo Lopes
- Alex Hernandez-Garcia
- Maithra Raghu
- Paroma Varma
- Han-Jia Ye
Papers from Henry R. Ehrenberg's perspective
Ordered by how strongly the researchers Henry R. Ehrenberg 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Snorkel: Rapid Training Data Creation with Weak Supervision (2017) — 1,174 citations
- Learning to Compose Domain-Specific Transformations for Data Augmentation (2017) — 367 citations
- Snorkel: rapid training data creation with weak supervision (2019) — 277 citations
- Data programming with DDLite: putting humans in a different part of the loop (2016) — 29 citations
- Snorkel: Fast Training Set Generation for Information Extraction (2017) — 95 citations