Sebastian Risi

Artificial Intelligence · 2009–2022

A researcher in the Science corpus: 17 papers; trusts 245 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 305 citations into it; #4,000 of 35,345 by trust here, #5,424 by citations
• Cognitive Neuroscience — 125 citations into it
• Electrical and Electronic Engineering — 86 citations into it; #1,964 of 16,539 by trust here, #8,322 by citations
• Cellular and Molecular Neuroscience — 81 citations into it
• Reinforcement Learning — 77 citations into it
• Computer Vision and Pattern Recognition — 72 citations into it
• Model Architectures — 37 citations into it; #1,137 of 2,278 by trust here, #1,726 by citations
• Neuroscience — 35 citations into it

Whom Sebastian Risi trusts overall

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

1. Kenneth O. Stanley
2. Risto Miikkulainen
3. Joel Lehman
4. J. Urgen Schmidhuber
5. Geoffrey E. Hinton
6. Dario Floreano
7. Andrea Soltoggio
8. Yoshua Bengio
9. Alex Krizhevsky
10. Alex Graves

Who trusts Sebastian Risi overall

1. Kenneth O. Stanley
2. Andrea Soltoggio
3. Joel Lehman
4. Paul Tonelli
5. Oliver J. Coleman
6. Jean-Baptiste Mouret
7. Rasmus Boll Greve
8. Jeff Clune
9. Justin K. Pugh
10. Alan D. Blair

Papers from Sebastian Risi's perspective

Ordered by how strongly the researchers Sebastian Risi 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation (2018) — 205 citations
• Learning macromanagement in starcraft from replays using deep learning (2017) — 73 citations
• Single-unit pattern generators for quadruped locomotion (2013) — 41 citations
• Indirectly Encoding Neural Plasticity as a Pattern of Local Rules (2010) — 102 citations
• How novelty search escapes the deceptive trap of learning to learn (2009) — 95 citations
• Increasing generality in machine learning through procedural content generation (2019) — 149 citations
• Breeding a diversity of Super Mario behaviors through interactive evolution (2016) — 13 citations
• Evolving plastic neural networks with novelty search (2010) — 99 citations
• A unified approach to evolving plasticity and neural geometry (2012) — 56 citations
• Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory (2017) — 25 citations
• Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017) — 149 citations
• Evolving Neural Turing Machines for Reward-based Learning (2016) — 42 citations
• Biological underpinnings for lifelong learning machines (2022) — 294 citations
• Automated Curriculum Learning by Rewarding Temporally Rare Events (2018) — 20 citations
• Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation (2018) — 240 citations
• Continual Learning through Evolvable Neural Turing Machines (2016)
• Guided self-organization in indirectly encoded and evolving topographic maps (2014) — 9 citations