Luca Pion-Tonachini
Cognitive Neuroscience · 2018–2020
A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 28 citations into it; #5,204 of 10,852 by trust here, #4,925 by citations
- Signal Processing — 13 citations into it; #5,478 of 7,572 by trust here, #2,990 by citations
- Artificial Intelligence — 9 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Generative Models — 6 citations into it
- Radiology, Nuclear Medicine and Imaging — 5 citations into it
Whom Luca Pion-Tonachini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arnaud Delorme
- Scott Makeig
- Augustus Odena
- Christopher Jennison
- Andrea Vedaldi
- Alec Radford
- T. Mullen
- Christian Andreas Kothe
- Nima Bigdely Shamlo
- Geoffrey E. Hinton
Who trusts Luca Pion-Tonachini overall
- Chi-Yuan Chang
- Paula Pena
- A. Hill
- T. Adamovich
- Saskia Wilken
- J. Ossandón
- Sheng-Hsiou Hsu
- Yoritaka Akimoto
- Ana F. Palenciano
- Gillian M. Clark
Papers from Luca Pion-Tonachini's perspective
Ordered by how strongly the researchers Luca Pion-Tonachini trusts endorse each paper. Same corpus, a different order for every researcher.
- 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings (2020) — 471 citations
- ICLabel: An automated electroencephalographic independent component classifier, dataset, and website (2019) — 1,925 citations
- Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal (2018) — 299 citations