Andrew B. Goldberg

Artificial Intelligence · 2006–2009

A researcher in the Science corpus: 2 papers; trusts 49 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #4,840 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it; #760 of 3,145 by trust here, #972 by citations
• Natural Language Processing — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Andrew B. Goldberg trusts overall

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

1. Thorsten Joachims
2. Avrim Blum
3. Alex Smola
4. Peter D. Turney
5. Bernhard Schölkopf
6. Olivier Chapelle
7. Bo Pang
8. Yoshua Bengio
9. Minqing Hu
10. Geoffrey E. Hinton

Who trusts Andrew B. Goldberg overall

1. Yann LeCun
2. Andrew L. Maas
3. Burr Settles
4. Andrey Kan
5. Jing Gao
6. Maite Taboada
7. Bhiksha Raj
8. Chelsea Finn
9. Alan Ramponi
10. Maxime Peyrard

Papers from Andrew B. Goldberg's perspective

Ordered by how strongly the researchers Andrew B. Goldberg 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. 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

• Introduction to Semi-Supervised Learning (2009) — 2,315 citations
• Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization (2006) — 374 citations