Patrick Shafto

Artificial Intelligence · 2008–2021

A researcher in the Science corpus: 8 papers; trusts 35 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #27,522 of 35,345 by trust here, #20,377 by citations
• Developmental and Educational Psychology — 41 citations into it; #3,041 of 8,223 by trust here, #1,376 by citations
• Natural Language Processing — 30 citations into it
• Reinforcement Learning — 28 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Cultural Studies — 18 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Education — 13 citations into it; #10,854 of 11,309 by trust here, #4,358 by citations

Whom Patrick Shafto trusts overall

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

1. T. Griffiths
2. Michael Tomasello
3. Josh Tenenbaum
4. Martin A. Zinkevich
5. György Gergely
6. Christophe Andrieu
7. Gergely Csibra
8. Melissa Koenig
9. V. Horner
10. Radford M. Neal

Who trusts Patrick Shafto overall

1. Xiaojin Zhu
2. Smitha Milli
3. Mark K. Ho
4. Felix Hill
5. Pieter Abbeel
6. Marie Devaine
7. Jaime Fernández Fisac
8. Michael L. Littman
9. Shane T. Mueller
10. Manu Kapur

Papers from Patrick Shafto's perspective

Ordered by how strongly the researchers Patrick Shafto 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A rational account of pedagogical reasoning: teaching by, and learning from, examples. (2014) — 300 citations
• The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. (2011) — 662 citations
• Interactive Learning from Activity Description (2021) — 36 citations
• Toward a general, scaleable framework for Bayesian teaching with applications to topic models (2016) — 13 citations
• BayesDB: A probabilistic programming system for querying the probable implications of data (2015) — 39 citations
• Learning From Others (2012) — 86 citations
• Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. (2011) — 221 citations
• Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations (2008)