Xiao Wang

Artificial Intelligence · The Web Conference · 2008–2023

A researcher in the Science corpus: 31 papers; trusts 809 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 687 citations into it; #11,480 of 35,345 by trust here, #2,476 by citations
• Computer Vision and Pattern Recognition — 447 citations into it
• Representation & Self-Supervised Learning — 154 citations into it
• Statistical and Nonlinear Physics — 151 citations into it; #2,958 of 10,127 by trust here, #846 by citations
• Graph Learning — 118 citations into it; #666 of 693 by trust here, #470 by citations
• Evaluation & Benchmarks — 99 citations into it
• Optimization & Training — 75 citations into it
• Information Systems — 73 citations into it; #5,132 of 8,771 by trust here, #2,539 by citations

Whom Xiao 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. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Max Welling
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Xiao Wang overall

1. Z. Ye
2. Min Xie
3. Shirui Pan
4. Zhenyu Liu
5. Nan Chen
6. Xiaolin Wang
7. Chuan Shi
8. Yu Wang
9. Ziniu Hu
10. Yuhui Ding

Papers from Xiao Wang's perspective

Ordered by how strongly the researchers Xiao 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• Simple Open-Vocabulary Object Detection with Vision Transformers (2022) — 421 citations
• LiT: Zero-Shot Transfer with Locked-image text Tuning (2021) — 757 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Simple Open-Vocabulary Object Detection (2022) — 437 citations
• Community Preserving Network Embedding (2017) — 922 citations
• Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization (2016) — 198 citations
• Beyond Low-frequency Information in Graph Convolutional Networks (2021) — 808 citations
• On the asymptotics of penalized spline smoothing (2011) — 57 citations
• Wiener processes with random effects for degradation data (2010) — 403 citations
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks (2020) — 616 citations
• An Inverse Gaussian Process Model for Degradation Data (2010) — 362 citations
• Recent developments in the PySCF program package. (2020) — 1,259 citations
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020) — 454 citations
• Three Towers: Flexible Contrastive Learning with Pretrained Image Models (2023)
• Federated Class-Incremental Learning (2022) — 299 citations
• Heterogeneous Graph Structure Learning for Graph Neural Networks (2021) — 319 citations
• Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization (2021) — 68 citations
• Northeastern University (2021) — 302 citations