John E. Hummel
Cognitive Science & Psychology · 1992–2019
A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Science & Psychology — 20 citations into it
- Neuroscience — 18 citations into it
- Artificial Intelligence — 17 citations into it; #10,464 of 35,345 by trust here, #10,667 by citations
- Cognitive Neuroscience — 12 citations into it; #2,995 of 10,852 by trust here, #4,260 by citations
- Developmental and Educational Psychology — 9 citations into it; #5,907 of 8,223 by trust here, #3,376 by citations
- Computer Vision and Pattern Recognition — 8 citations into it
- Model Architectures — 8 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
Whom John E. Hummel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Gary F. Marcus
- David E. Rumelhart
- Keith J. Holyoak
- Nelson Cowan
- James L. McClelland
- Irving Biederman
- Dedre Gentner
- A. B. Bonds
- Christoph von der Malsburg
Who trusts John E. Hummel overall
- Leonidas A. A. Doumas
- E. T. Rolls
- Seth Chin-Parker
- Keith J. Holyoak
- Franklin Chang
- Klaus Greff
- Sjoerd van Steenkiste
- Gary F. Marcus
- Catherine M. Sandhofer
- Gordon D. Logan
Papers from John E. Hummel's perspective
Ordered by how strongly the researchers John E. Hummel trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- A symbolic-connectionist theory of relational inference and generalization. (2003) — 555 citations
- A theory of the discovery and predication of relational concepts. (2008) — 390 citations
- Relation learning in a neurocomputational architecture supports cross-domain transfer (2019) — 6 citations
- A Solution to the Binding Problem for Compositional Connectionism (2004) — 30 citations
- Distributing structure over time (1993)
- Dynamic binding in a neural network for shape recognition. (1992)