Derek Greene

Artificial Intelligence · 2006–2015

A researcher in the Science corpus: 5 papers; trusts 44 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #10,225 of 35,345 by trust here, #17,776 by citations
• Natural Language Processing — 17 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #5,776 of 10,127 by trust here, #7,402 by citations
• Sociology and Political Science — 10 citations into it
• Information Systems — 8 citations into it
• Molecular Biology — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Derek Greene trusts overall

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

1. Christopher K. I. Williams
2. Alex Smola
3. David M. Blei
4. Bernhard Schölkopf
5. Andrea Lancichinetti
6. Daniel D. Lee
7. Alexander Strehl
8. Geoffrey E. Hinton
9. Tomas Mikolov
10. Michael I. Jordan

Who trusts Derek Greene overall

1. Maarten Grootendorst
2. Kyunghyun Cho
3. Tian Wang
4. Yasumasa Onoe
5. Shervin Minaee
6. Punam Bedi
7. Jialu Liu
8. Joey Tianyi Zhou
9. Erik Cambria
10. Santo Fortunato

Papers from Derek Greene's perspective

Ordered by how strongly the researchers Derek Greene trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• Practical solutions to the problem of diagonal dominance in kernel document clustering (2006) — 723 citations
• Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems (2015) — 172 citations
• An analysis of the coherence of descriptors in topic modeling (2015) — 343 citations
• Normalized Mutual Information to evaluate overlapping community finding algorithms (2011) — 366 citations
• Tracking the Evolution of Communities in Dynamic Social Networks (2010) — 547 citations