Richard N. Aslin

Cognitive Science & Psychology · 1975–2014

A researcher in the Science corpus: 11 papers; trusts 24 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Science & Psychology — 18 citations into it; #420 of 582 by trust here, #415 by citations
• Artificial Intelligence — 17 citations into it; #7,141 of 35,345 by trust here, #10,065 by citations
• Cognitive Neuroscience — 17 citations into it; #2,436 of 10,852 by trust here, #2,336 by citations
• Developmental and Educational Psychology — 16 citations into it; #582 of 8,223 by trust here, #1,026 by citations
• Experimental and Cognitive Psychology — 12 citations into it; #777 of 9,731 by trust here, #1,334 by citations
• Cultural Studies — 10 citations into it
• Neuroscience — 7 citations into it
• Language and Linguistics — 5 citations into it; #1,963 of 3,093 by trust here, #1,865 by citations

Whom Richard N. Aslin trusts overall

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

1. A. D. Di Sciullo
2. Elissa L. Newport
3. Fei Xu
4. Jenny R. Saffran
5. Josh Tenenbaum
6. Geoffrey E. Hinton
7. Gary F. Marcus
8. Marvin M. Chun
9. David John Cameron MacKay
10. Noam Chomsky

Who trusts Richard N. Aslin overall

1. Jenny R. Saffran
2. Patricia K. Kuhl
3. Amanda Seidl
4. Divya Tomar
5. Linda B. Smith
6. Yuliya Tarabalka
7. Barbara Hohle
8. József Fiser
9. Mark S. Seidenberg
10. Emmanuel M. Pothos

Papers from Richard N. Aslin's perspective

Ordered by how strongly the researchers Richard N. Aslin 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex (2012) — 664 citations
• Bayesian learning of visual chunks by human observers (2008) — 276 citations
• Statistical learning of higher-order temporal structure from visual shape sequences. (2002) — 630 citations
• From the Cover: Statistical learning of new visual feature combinations by infants (2002) — 506 citations
• Models of Word Segmentation in Fluent Maternal Speech to Infants (2014) — 104 citations
• Statistical learning: From acquiring specific items to forming general rules (2012) — 607 citations
• The Weckud Wetch of the Wast: Lexical Adaptation to a Novel Accent (2008) — 316 citations
• Learning at a distance I. Statistical learning of non-adjacent dependencies. (2004) — 721 citations
• Bayesian integration of visual and auditory signals for spatial localization. (2003) — 466 citations
• Statistical Learning by 8-Month-Old Infants (1996)
• Sensitive period for the development of human binocular vision (1975)