Andrea Soltoggio

Cognitive Neuroscience · 2007–2022

A researcher in the Science corpus: 16 papers; trusts 584 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 256 citations into it; #401 of 10,852 by trust here, #2,087 by citations
• Artificial Intelligence — 205 citations into it; #17,820 of 35,345 by trust here, #20,836 by citations
• Cellular and Molecular Neuroscience — 192 citations into it; #183 of 2,998 by trust here, #986 by citations
• Electrical and Electronic Engineering — 117 citations into it; #690 of 16,539 by trust here, #5,200 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #11,743 of 21,575 by trust here, #15,162 by citations
• Neuroscience — 70 citations into it; #115 of 487 by trust here, #410 by citations
• Molecular Biology — 46 citations into it
• Representation & Self-Supervised Learning — 46 citations into it

Whom Andrea Soltoggio trusts overall

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

1. Dario Floreano
2. Kenneth O. Stanley
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Wulfram Gerstner
6. John A. Bullinaria
7. Claudio Mattiussi
8. Alex Krizhevsky
9. Kenji Doya
10. Olaf Sporns

Who trusts Andrea Soltoggio overall

1. Paul Tonelli
2. Kenneth O. Stanley
3. Jean-Baptiste Mouret
4. Sebastian Risi
5. Jeff Clune
6. Gerard David Howard
7. Yuri Lenon Barbosa Nogueira
8. Massimo Caccia
9. J. Gustafsson
10. Joel Lehman

Papers from Andrea Soltoggio's perspective

Ordered by how strongly the researchers Andrea Soltoggio 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation (2018) — 104 citations
• Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017) — 149 citations
• Evolving neuromodulatory topologies for reinforcement learning-like problems (2007) — 83 citations
• Neural Plasticity and Minimal Topologies for Reward-Based Learning (2008) — 16 citations
• From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012) — 33 citations
• Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013) — 20 citations
• Solving the Distal Reward Problem with Rare Correlations (2013) — 33 citations
• Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014) — 31 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• Building Efficient Deep Hebbian Networks for Image Classification Tasks (2017) — 11 citations
• Online Representation Learning with Multi-layer Hebbian Networks for Image Classification Tasks (2017) — 3 citations
• Real-time Hebbian Learning from Autoencoder Features for Control Tasks (2014) — 5 citations
• Learning the rules of a game: Neural conditioning in human-robot interaction with delayed rewards (2013) — 11 citations
• Novelty of behaviour as a basis for the neuro-evolution of operant reward learning (2009) — 24 citations
• Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008) — 5 citations
• Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios (2008) — 154 citations