Jiaqi W. Ma
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 38 citations into it; #25,333 of 35,345 by trust here, #28,481 by citations
- Graph Learning — 19 citations into it
- Representation & Self-Supervised Learning — 17 citations into it
- Statistical and Nonlinear Physics — 14 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #20,383 of 21,575 by trust here, #10,334 by citations
- Optimization & Training — 8 citations into it; #2,513 of 2,601 by trust here, #1,385 by citations
- Information Systems — 7 citations into it; #4,923 of 8,771 by trust here, #2,471 by citations
- Molecular Biology — 5 citations into it; #10,658 of 17,773 by trust here, #17,184 by citations
Whom Jiaqi W. Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- R. Caruana
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Jimmy Ba
- Mathias Niepert
Who trusts Jiaqi W. Ma overall
- Zhe Zhao
- Lin Wang
- Yi Tay
- Barret Zoph
- Kuk-Jin Yoon
- Hongyan Tang
- Ruihan Yang
- Xi Lin
- Krzysztof Maziarz
- Yuyan Wang
Papers from Jiaqi W. Ma's perspective
Ordered by how strongly the researchers Jiaqi W. Ma 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018) — 1,663 citations
- Graph Representation Learning via Multi-task Knowledge Distillation (2019) — 19 citations
- DeepCas: An End-to-end Predictor of Information Cascades (2016) — 378 citations