Thomas Miconi
Artificial Intelligence · 2008–2020
A researcher in the Science corpus: 5 papers; trusts 78 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 64 citations into it; #22,675 of 35,345 by trust here, #19,439 by citations
- Cognitive Neuroscience — 41 citations into it; #3,265 of 10,852 by trust here, #4,528 by citations
- Computer Vision and Pattern Recognition — 30 citations into it
- Model Architectures — 24 citations into it
- Cellular and Molecular Neuroscience — 21 citations into it; #2,918 of 2,998 by trust here, #2,534 by citations
- Representation & Self-Supervised Learning — 18 citations into it
- Optimization & Training — 16 citations into it; #1,890 of 2,601 by trust here, #1,715 by citations
- Electrical and Electronic Engineering — 12 citations into it; #3,845 of 16,539 by trust here, #7,640 by citations
Whom Thomas Miconi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- John H. Holland
- Geoffrey E. Hinton
- Yoshua Bengio
- Sepp Hochreiter
- Karl Sims
- Hiroaki Kitano
- Kenneth O. Stanley
- Robert Axelrod
- Faustino J. Gomez
Who trusts Thomas Miconi overall
- Jeff Clune
- Sebastian Flennerhag
- Junhyuk Oh
- Shawn Beaulieu
- Kenneth O. Stanley
- Nithin Holla
- Imanol Schlag
- Kevin Lu
- Jonas Rothfuss
- Andrei A. Rusu
Papers from Thomas Miconi's perspective
Ordered by how strongly the researchers Thomas Miconi 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Differentiable plasticity: training plastic neural networks with backpropagation (2018) — 196 citations
- Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity (2018) — 106 citations
- Learning to Continually Learn (2020) — 166 citations
- A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure (2016) — 22 citations
- The road to everywhere : Evolution, complexity and progress in natural and artificial systems (2008) — 16 citations