Ziheng Wang

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 210 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #11,985 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #7,993 of 21,575 by trust here, #15,955 by citations
• Efficiency & Compression — 25 citations into it
• Natural Language Processing — 14 citations into it
• Large Language Models — 11 citations into it
• Model Architectures — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Ziheng Wang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Song Han
6. Yann LeCun
7. Ilya Sutskever
8. Kaiming He
9. Max Welling
10. Jimmy Ba

Who trusts Ziheng Wang overall

1. Saeid Motiian
2. J. Scott McCarley
3. Pichao Wang
4. Gianfranco Doretto
5. Xinyin Ma
6. Mengzhou Xia
7. François Lagunas
8. Mojtaba Valipour
9. Xiao Zhou
10. Zi-Mei Yao

Papers from Ziheng Wang's perspective

Ordered by how strongly the researchers Ziheng Wang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Classifier learning with hidden information (2015) — 58 citations
• Structured Pruning of Large Language Models (2019) — 354 citations
• A generative restricted Boltzmann machine based method for high-dimensional motion data modeling (2015) — 53 citations