Gianluca Baldassarre
Cognitive Neuroscience · 2007–2016
A researcher in the Science corpus: 15 papers; trusts 143 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 348 citations into it; #1,592 of 10,852 by trust here, #3,180 by citations
- Cellular and Molecular Neuroscience — 158 citations into it
- Reinforcement Learning — 114 citations into it; #673 of 2,242 by trust here, #1,174 by citations
- Artificial Intelligence — 108 citations into it; #10,851 of 35,345 by trust here, #12,761 by citations
- Social Psychology — 83 citations into it; #12,356 of 15,694 by trust here, #9,333 by citations
- Neuroscience — 67 citations into it; #412 of 487 by trust here, #368 by citations
- Cognitive Science & Psychology — 63 citations into it; #511 of 582 by trust here, #498 by citations
- Molecular Biology — 58 citations into it
Whom Gianluca Baldassarre trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrew Barto
- J. Urgen Schmidhuber
- Richard S. Sutton
- Peter S. Dayan
- Wolfram Schultz
- Pierre-Yves Oudeyer
- Satinder Singh
- Kevin N. Gurney
- Earl K. Miller
- Geoffrey E. Hinton
Who trusts Gianluca Baldassarre overall
- Pierre-Yves Oudeyer
- Adrien Baranes
- Manuel Lopes
- Marco Mirolli
- Brenden M. Lake
- Vieri Giuliano Santucci
- Levent Bayindir
- Sao Mai Nguyen
- Clément Moulin-Frier
- Jacqueline Gottlieb
Papers from Gianluca Baldassarre's perspective
Ordered by how strongly the researchers Gianluca Baldassarre 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)
- Reinforcement Learning: An Introduction
- 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
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Intrinsically Motivated Learning in Natural and Artificial Systems (2013) — 281 citations
- Intrinsic motivations and open-ended development in animals, humans, and robots: an overview (2014) — 68 citations
- TRoPICALS: a computational embodied neuroscience model of compatibility effects. (2010) — 128 citations
- Evolution and Learning in an Intrinsically Motivated Reinforcement Learning Robot (2007) — 64 citations
- The roles of the amygdala in the affective regulation of body, brain, and behaviour (2010) — 40 citations
- Self-Organized Coordinated Motion in Groups of Physically Connected Robots (2007) — 131 citations
- GRAIL: A Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning (2016) — 109 citations
- Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex (2016) — 387 citations
- Selection of cortical dynamics for motor behaviour by the basal ganglia (2015) — 46 citations
- Novelty or Surprise? (2013) — 369 citations
- The nucleus accumbens as a nexus between values and goals in goal-directed behavior: a review and a new hypothesis (2013) — 161 citations
- The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis (2013) — 17 citations
- Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model. (2013) — 60 citations
- What are intrinsic motivations? A biological perspective (2011) — 109 citations
- Evolving Childhood's Length and Learning Parameters in an Intrinsically Motivated Reinforcement Learning Robot (2007) — 40 citations