Liu Ziyin

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 182 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #22,783 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #14,169 of 21,575 by trust here, #15,529 by citations
• Model Architectures — 26 citations into it
• Natural Language Processing — 22 citations into it
• Optimization & Training — 19 citations into it
• Federated & Distributed Learning — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Multimodal Learning — 13 citations into it; #751 of 1,211 by trust here, #1,033 by citations

Whom Liu Ziyin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Graves
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Yann LeCun
9. Diederik P. Kingma
10. J. Urgen Schmidhuber

Who trusts Liu Ziyin overall

1. Cong Han
2. Rithesh Kumar
3. Sang-gil Lee
4. Amir Zadeh
5. Hubert Siuzdak
6. Devamanyu Hazarika
7. Percy Liang
8. Vinay S. Raghavan
9. Wei Ping
10. Prem Seetharaman

Papers from Liu Ziyin's perspective

Ordered by how strongly the researchers Liu Ziyin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Multimodal Language Analysis with Recurrent Multistage Fusion (2018) — 222 citations
• Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020) — 750 citations
• Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020) — 233 citations