Shimon Edelman
Artificial Intelligence · 1990–2016
A researcher in the Science corpus: 3 papers; trusts 78 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #25,033 of 35,345 by trust here, #27,551 by citations
- Cognitive Neuroscience — 19 citations into it
- Natural Language Processing — 13 citations into it
- Developmental and Educational Psychology — 9 citations into it
- Reinforcement Learning — 8 citations into it
- Cellular and Molecular Neuroscience — 7 citations into it
- Molecular Biology — 7 citations into it; #10,412 of 17,773 by trust here, #15,203 by citations
- Computer Vision and Pattern Recognition — 6 citations into it
Whom Shimon Edelman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- Andrew Barto
- Geoffrey E. Hinton
- Yoshua Bengio
- George V. Cybenko
- Ronan Collobert
- Richard S. Sutton
- Ilya Sutskever
- Matthew Botvinick
- Yann LeCun
Who trusts Shimon Edelman overall
- Tomaso A. Poggio
- Maximilian Riesenhuber
- Joel Z. Leibo
- Trevor Darrell
- Hiroshi Murase
- E. T. Rolls
- Fabio Anselmi
- Frank Jäkel
- Daniel J. Kersten
- Thomas Vetter
Papers from Shimon Edelman's perspective
Ordered by how strongly the researchers Shimon Edelman trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Unsupervised learning of natural languages (2009) — 333 citations
- The minority report: some common assumptions to reconsider in the modelling of the brain and behaviour (2016) — 37 citations
- A network that learns to recognize three-dimensional objects (1990)