Gideon S. Mann

Artificial Intelligence · 2001–2014

A researcher in the Science corpus: 9 papers; trusts 102 researchers and is trusted by 107 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #3,736 of 35,345 by trust here, #8,810 by citations
• Natural Language Processing — 31 citations into it; #1,545 of 5,688 by trust here, #3,421 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #653 of 3,145 by trust here, #1,671 by citations
• Optimization & Training — 17 citations into it; #1,236 of 2,601 by trust here, #975 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Molecular Biology — 7 citations into it

Whom Gideon S. Mann trusts overall

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

1. Andrew McCallum
2. Thorsten Joachims
3. Robert Malouf
4. Xiaojin Zhu
5. Yoshua Bengio
6. Kamal Nigam
7. Olivier Chapelle
8. Yves Grandvalet
9. Zoubin Ghahramani
10. Daniel Klein

Who trusts Gideon S. Mann overall

1. Burr Settles
2. Andrew McCallum
3. Gregory Druck
4. Shashank Srivastava
5. Percy Liang
6. Martin Wistuba
7. Giorgio Patrini
8. Aymeric Dieuleveut
9. Jakub Konecný
10. David Chiang

Papers from Gideon S. Mann's perspective

Ordered by how strongly the researchers Gideon S. Mann 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Learning from labeled features using generalized expectation criteria (2008) — 291 citations
• Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data (2010) — 221 citations
• Simple, robust, scalable semi-supervised learning via expectation regularization (2007) — 184 citations
• Looking Under the Hood: Tools for Diagnosing Your Question Answering Engine (2001) — 30 citations
• Unsupervised Personal Name Disambiguation (2003) — 376 citations
• Efficient Transfer Learning Method for Automatic Hyperparameter Tuning (2014) — 226 citations
• Distributed Training Strategies for the Structured Perceptron (2010) — 321 citations
• Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models (2009) — 311 citations
• Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields (2008) — 139 citations