Zhiwu Lu

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 863 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 475 citations into it; #10,371 of 35,345 by trust here, #4,393 by citations
• Computer Vision and Pattern Recognition — 412 citations into it; #19,636 of 21,575 by trust here, #4,438 by citations
• Representation & Self-Supervised Learning — 146 citations into it; #1,283 of 3,145 by trust here, #774 by citations
• Natural Language Processing — 139 citations into it
• Multimodal Learning — 91 citations into it
• Model Architectures — 63 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Molecular Biology — 29 citations into it

Whom Zhiwu Lu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ross B. Girshick
7. Karen Simonyan
8. Yann LeCun
9. Ilya Sutskever
10. Oriol Vinyals

Who trusts Zhiwu Lu overall

1. Yann LeCun
2. Dong-Hyun Lee
3. Zdenek Kalal
4. Bernhard Schölkopf
5. Dominik Janzing
6. Adel Javanmard
7. Ishan Misra
8. Rie Johnson
9. Mohammad Taher Pilehvar
10. Aliaksei Sandryhaila

Papers from Zhiwu Lu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Semi-Supervised Learning (2014) — 517 citations
• Learning Depth-Guided Convolutions for Monocular 3D Object Detection (2019) — 372 citations
• Recursive Visual Attention in Visual Dialog (2018) — 128 citations
• Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
• Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy (2019) — 140 citations
• WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training (2021) — 146 citations
• Towards artificial general intelligence via a multimodal foundation model (2021) — 331 citations
• Counterfactual VQA: A Cause-Effect Look at Language Bias (2020) — 539 citations
• Domain-Adaptive Few-Shot Learning (2020) — 93 citations
• Domain-Invariant Projection Learning for Zero-Shot Recognition (2018) — 50 citations
• CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction (2016) — 75 citations