Claudio Mattiussi
Molecular Biology · 2001–2010
A researcher in the Science corpus: 9 papers; trusts 31 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 32 citations into it; #1,795 of 17,773 by trust here, #7,911 by citations
- Artificial Intelligence — 24 citations into it; #4,653 of 35,345 by trust here, #9,092 by citations
- Cognitive Neuroscience — 20 citations into it; #1,260 of 10,852 by trust here, #4,260 by citations
- Cellular and Molecular Neuroscience — 16 citations into it; #291 of 2,998 by trust here, #1,454 by citations
- Electrical and Electronic Engineering — 8 citations into it; #1,214 of 16,539 by trust here, #7,175 by citations
- Neuroscience — 7 citations into it
- Genetics — 6 citations into it
- Neural Architecture Search & AutoML — 5 citations into it
Whom Claudio Mattiussi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kenneth O. Stanley
- Risto Miikkulainen
- Dario Floreano
- Richard S. Sutton
- J. Krichmar
- Andrea Soltoggio
- Kenji Doya
- Faustino J. Gomez
- Thomas Bäck
- Randall D. Beer
Who trusts Claudio Mattiussi overall
- Andrea Soltoggio
- Gerard David Howard
- Yuri Lenon Barbosa Nogueira
- Kenneth O. Stanley
- Sebastian Risi
- Peter Dürr
- Paul Tonelli
- E. D. Di Paolo
- Jean-Baptiste Mouret
- Carlos Brito
Papers from Claudio Mattiussi's perspective
Ordered by how strongly the researchers Claudio Mattiussi trusts endorse each paper. Same corpus, a different order for every researcher.
- Reinforcement Learning: An Introduction
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Learning to predict by the methods of temporal differences
- Long Short-Term Memory
- Evolving Neural Networks through Augmenting Topologies
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- GENERATIVE ADVERSARIAL NETS
Their papers
- Neuroevolution: from architectures to learning (2008) — 761 citations
- Evolving neuromodulatory topologies for reinforcement learning-like problems (2007) — 83 citations
- Revealing strengths and weaknesses of methods for gene network inference (2010) — 795 citations
- Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods (2009) — 491 citations
- Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots (2001) — 170 citations
- Analog Genetic Encoding for the Evolution of Circuits and Networks (2007) — 157 citations
- Genetic Representation and Evolvability of Modular Neural Controllers (2010) — 29 citations
- Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (2008) — 236 citations
- Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios (2008) — 154 citations