Sergey M. Plis
Cognitive Neuroscience · 2013–2016
A researcher in the Science corpus: 9 papers; trusts 37 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 88 citations into it; #5,708 of 10,852 by trust here, #4,443 by citations
- Artificial Intelligence — 37 citations into it
- Computer Vision and Pattern Recognition — 21 citations into it
- Molecular Biology — 17 citations into it
- Signal Processing — 14 citations into it
- Statistics and Probability — 12 citations into it
- Optimization & Training — 11 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
Whom Sergey M. Plis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Helmut Ltkepohl
- Geoffrey E. Hinton
- David Danks
- Clive W. J. Granger
- Trevor J. Hastie
- Alex Krizhevsky
- Mark Voortman
- Radford M. Neal
- Yoshua Bengio
- Denver Dash
Who trusts Sergey M. Plis overall
- Mingming Gong
- Kun Zhang
- Heung-Il Suk
- Clark Glymour
- Dinggang Shen
- David Danks
- Yuhui Du
- Antti Hyttinen
- Ali Shojaie
- Ruocheng Guo
Papers from Sergey M. Plis's perspective
Ordered by how strongly the researchers Sergey M. Plis 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Causal Discovery from Subsampled Time Series Data by Constraint Optimization (2016) — 46 citations
- Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data (2016) — 495 citations
- Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016) — 852 citations
- Mesochronal Structure Learning (2015) — 11 citations
- Rate-Agnostic (Causal) Structure Learning (2015) — 28 citations
- Tracking whole-brain connectivity dynamics in the resting state. (2014) — 447 citations
- Supplementary Material to: Tracking Whole-Brain Connectivity Dynamics in the Resting State. (2013) — 2,303 citations
- Learning Causal Structure from Undersampled Time Series (2013) — 61 citations
- Deep learning for neuroimaging: a validation study (2013) — 604 citations