Robert A. Jacobs

Artificial Intelligence · 1987–2011

A researcher in the Science corpus: 9 papers; trusts 21 researchers and is trusted by 267 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #33,404 of 35,345 by trust here, #33,294 by citations
• Probabilistic & Bayesian Methods — 11 citations into it; #107 of 1,525 by trust here, #154 by citations
• Developmental and Educational Psychology — 7 citations into it; #7,328 of 8,223 by trust here, #7,501 by citations
• Statistical Learning Theory — 6 citations into it; #542 of 1,038 by trust here, #874 by citations
• Model Architectures — 5 citations into it; #896 of 2,278 by trust here, #2,009 by citations

Whom Robert A. Jacobs trusts overall

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

1. Michael I. Jordan
2. Jerome H. Friedman
3. John Scott Bridle
4. Paul J. Werbos
5. Geoffrey E. Hinton
6. David E. Rumelhart
7. Steve R. Waterhouse
8. David John Cameron MacKay
9. Steven J. Nowlan
10. J. Ross Quinlan

Who trusts Robert A. Jacobs overall

1. Yoshua Bengio
2. L. V. D. van der Maaten
3. Geoffrey E. Hinton
4. J. Urgen Schmidhuber
5. Zoubin Ghahramani
6. Sergey Levine
7. Richard S. Sutton
8. Dacheng Tao
9. Ruslan Salakhutdinov
10. Alan L. Yuille

Papers from Robert A. Jacobs's perspective

Ordered by how strongly the researchers Robert A. Jacobs 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Bayesian learning theory applied to human cognition. (2011) — 130 citations
• Bayesian integration of visual and auditory signals for spatial localization. (2003) — 466 citations
• Bias/Variance Analyses of Mixtures-of-Experts Architectures (1997)
• Methods For Combining Experts' Probability Assessments (1995)
• Hierarchical Mixtures of Experts and the EM Algorithm (1993)
• Adaptive Mixtures of Local Experts (1991)
• A Competitive Modular Connectionist Architecture (1990)
• Learning to Control an Unstable System with Forward Modeling (1989)
• Increased rates of convergence through learning rate adaptation (1987)