Dorina Thanou
Artificial Intelligence · 2013–2018
A researcher in the Science corpus: 3 papers; trusts 37 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 28 citations into it; #24,156 of 35,345 by trust here, #26,682 by citations
- Statistical and Nonlinear Physics — 16 citations into it; #6,488 of 10,127 by trust here, #6,727 by citations
- Computer Networks and Communications — 12 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
- Statistics and Probability — 12 citations into it
- Graph Learning — 10 citations into it
- Computational Theory and Mathematics — 9 citations into it
- Computational Mechanics — 8 citations into it
Whom Dorina Thanou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lin Xiao
- Stephen P. Boyd
- Jun Fang
- Yoshua Bengio
- Jerome H. Friedman
- Ulrike von Luxburg
- Ruggero Carli
- Nicolai Meinshausen
- José M. F. Moura
- Robert Tibshirani
Who trusts Dorina Thanou overall
- Chang-Shen Lee
- Santiago Segarra
- Gonzalo Mateos
- Hilmi E. Egilmez
- Nicolò Michelusi
- Xiaowen Dong
- Peter Berger
Papers from Dorina Thanou's perspective
Ordered by how strongly the researchers Dorina Thanou 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
- 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
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Learning Graphs From Data: A Signal Representation Perspective (2018) — 429 citations
- Learning Laplacian Matrix in Smooth Graph Signal Representations (2014) — 705 citations
- Distributed Average Consensus With Quantization Refinement (2013) — 63 citations