Takanori Maehara
Artificial Intelligence · 2010–2019
A researcher in the Science corpus: 7 papers; trusts 110 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 44 citations into it; #17,874 of 35,345 by trust here, #18,586 by citations
- Natural Language Processing — 24 citations into it
- Representation & Self-Supervised Learning — 22 citations into it
- Graph Learning — 18 citations into it
- Statistical and Nonlinear Physics — 14 citations into it
- Statistics and Probability — 10 citations into it; #8,708 of 12,932 by trust here, #11,487 by citations
- Computational Theory and Mathematics — 9 citations into it
- Optimization & Training — 9 citations into it
Whom Takanori Maehara trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Y. Saad
- Yoshua Bengio
- Diederik P. Kingma
- Tomas Mikolov
- Geoffrey E. Hinton
- Jean B. Lasserre
- Richard Socher
- Ilya Sutskever
- Alex Krizhevsky
Who trusts Takanori Maehara overall
- Satoshi Hara
- Lingxiao Zhao
- Hu Xu
- Jingyuan Sun
- Vijay Prakash Dwivedi
- Ulrich Matchi Aïvodji
- Yftah Ziser
- Leman Akoglu
- Masakazu Ishihata
Papers from Takanori Maehara's perspective
Ordered by how strongly the researchers Takanori Maehara 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Revisiting Graph Neural Networks: All We Have is Low-Pass Filters (2019) — 526 citations
- Computing Personalized PageRank Quickly by Exploiting Graph Structures (2014) — 84 citations
- Enumerate Lasso Solutions for Feature Selection (2017) — 41 citations
- Finding Alternate Features in Lasso (2016) — 5 citations
- Pretending Fair Decisions via Stealthily Biased Sampling (2019) — 3 citations
- Unsupervised Cross-Domain Word Representation Learning (2015) — 76 citations
- A numerical algorithm for block-diagonal decomposition of matrix *-algebras with general irreducible components (2010) — 51 citations