Hava T. Siegelmann

Cognitive Neuroscience · 1991–2022

A researcher in the Science corpus: 13 papers; trusts 135 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 286 citations into it; #8,629 of 10,852 by trust here, #4,022 by citations
• Artificial Intelligence — 192 citations into it; #12,619 of 35,345 by trust here, #12,244 by citations
• Cellular and Molecular Neuroscience — 125 citations into it
• Computer Vision and Pattern Recognition — 115 citations into it
• Meta & Continual Learning — 86 citations into it; #347 of 1,041 by trust here, #342 by citations
• Neuroscience — 69 citations into it; #158 of 487 by trust here, #154 by citations
• Representation & Self-Supervised Learning — 48 citations into it
• Experimental and Cognitive Psychology — 39 citations into it

Whom Hava T. Siegelmann trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. George V. Cybenko
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Yuhai Wu
8. J. Urgen Schmidhuber
9. Anthony V. Robins
10. Kaiming He

Who trusts Hava T. Siegelmann overall

1. J. Urgen Schmidhuber
2. Patrick Putzky
3. Jeff Clune
4. Henrik Jacobsson
5. Michael Hahn
6. Max Welling
7. Andrew Kyle Lampinen
8. Imanol Schlag
9. Marc Rußwurm
10. C. Lee Giles

Papers from Hava T. Siegelmann's perspective

Ordered by how strongly the researchers Hava T. Siegelmann 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Flexible Kernel Memory (2010) — 21 citations
• The Super-Turing Computational Power of plastic Recurrent Neural Networks (2014) — 58 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021) — 152 citations
• A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex (2020) — 52 citations
• Brain-inspired replay for continual learning with artificial neural networks (2020) — 604 citations
• The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions (2015) — 118 citations
• The Computational Model (2013) — 84 citations
• Conspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach (2010) — 16 citations
• Support Vector Clustering (2002) — 1,579 citations
• Computational capabilities of recurrent NARX neural networks (1997)
• On the computational power of neural nets (1992)
• Turing computability with neural nets (1991)