Joseph Jay Williams

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 10 papers; trusts 25 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #9,509 of 35,345 by trust here, #11,327 by citations
• Developmental and Educational Psychology — 43 citations into it; #1,741 of 8,223 by trust here, #2,859 by citations
• Computer Science Applications — 23 citations into it; #1,393 of 1,855 by trust here, #991 by citations
• Safety, Robustness & Interpretability — 21 citations into it
• History and Philosophy of Science — 14 citations into it
• Education — 13 citations into it
• Cognitive Neuroscience — 10 citations into it
• Cognitive Science & Psychology — 10 citations into it; #199 of 582 by trust here, #310 by citations

Whom Joseph Jay Williams trusts overall

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

1. Tania Lombrozo
2. Michelene T. H. Chi
3. Thomas Bartelborth
4. Michael Strevens
5. Robert S. Siegler
6. J. Langford
7. Olivier Chapelle
8. Bethany Rittle-Johnson
9. Judea Pearl
10. Douglas L. Medin

Who trusts Joseph Jay Williams overall

1. Tania Lombrozo
2. Seth Chin-Parker
3. Finale Doshi-Velez
4. Brenden M. Lake
5. Julie A Cantelon
6. Daniel A. Wilkenfeld
7. Jonathan T. Shemwell
8. Shane T. Mueller
9. Been Kim
10. Josh Tenenbaum

Papers from Joseph Jay Williams's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• The role of explanation in discovery and generalization: evidence from category learning (2010) — 224 citations
• Evaluating computational models of explanation using human judgments (2013) — 30 citations
• Sequential Explanations with Mental Model-Based Policies (2020) — 16 citations
• Mining Big Data in Education: Affordances and Challenges (2020) — 313 citations
• The Impact of Adding Textual Explanations to Next-step Hints in a Novice Programming Environment (2019) — 45 citations
• AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning (2016) — 177 citations
• HarvardX and MITx: Two Years of Open Online Courses Fall 2012-Summer 2014 (2015) — 353 citations
• The hazards of explanation: overgeneralization in the face of exceptions. (2013) — 90 citations
• Why does explaining help learning? Insight from an explanation impairment effect (2010) — 32 citations
• Explanation and Prior Knowledge Running Head: Explanation and Prior Knowledge Explanation and Prior Knowledge Interact to Guide Learning Explanation and Prior Knowledge Interact to Guide Learning