Samuel Rönnqvist
Natural Language Processing · 2015–2017
A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 29 citations into it
- Artificial Intelligence — 26 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Finance — 7 citations into it; #5,297 of 10,518 by trust here, #9,725 by citations
- Computer Vision and Pattern Recognition — 6 citations into it
- Economics and Econometrics — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Samuel Rönnqvist trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tomas Mikolov
- Quoc V. Le
- David E. Rumelhart
- Geoffrey E. Hinton
- Yurii E. Nesterov
- William Yang Wang
- Yoshua Bengio
- F. Betz
- Ronald J. Williams
- J. Urgen Schmidhuber
Who trusts Samuel Rönnqvist overall
- A. Ozbayoglu
- A. Navon
- M. Ugur Gudelek
- Paola Cerchiello
- Peter Sarlin
- Omer Berat Sezer
- Yosi Keller
- Giancarlo Nicola
Papers from Samuel Rönnqvist's perspective
Ordered by how strongly the researchers Samuel Rönnqvist 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Detect & Describe: Deep Learning of Bank Stress in the News (2015) — 12 citations
- Deep learning bank distress from news and numerical financial data (2017) — 13 citations
- Bank distress in the news: Describing events through deep learning (2016) — 56 citations