Chenliang Li

Artificial Intelligence · with Huang · 2019–2023

A researcher in the Science corpus: 11 papers; trusts 457 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 293 citations into it; #32,261 of 35,345 by trust here, #5,252 by citations
• Computer Vision and Pattern Recognition — 267 citations into it
• Multimodal Learning — 145 citations into it
• Natural Language Processing — 123 citations into it
• Large Language Models — 68 citations into it
• Evaluation & Benchmarks — 52 citations into it
• Model Architectures — 42 citations into it
• Representation & Self-Supervised Learning — 39 citations into it

Whom Chenliang Li trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Jacob Devlin
6. Diederik P. Kingma
7. Ilya Sutskever
8. Ross B. Girshick
9. Yann LeCun
10. Alec Radford

Who trusts Chenliang Li overall

1. Haiyang Xu
2. Ming Yan
3. Izzeddin Gur
4. Yupan Huang
5. Wayne Xin Zhao
6. Xiao Wang
7. Qinghao Ye
8. Jiasen Lu
9. Geewook Kim
10. Ruiyang Ren

Papers from Chenliang Li's perspective

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

Their papers

• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality (2023) — 1,270 citations
• E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning (2021) — 130 citations
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022) — 295 citations
• StructuralLM: Structural Pre-training for Form Understanding (2021) — 149 citations
• SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels (2021) — 41 citations
• PALM: Pre-training an Autoencoding&autoregressive Language Model for Context-conditioned Generation (2020) — 78 citations
• Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training (2021) — 11 citations
• Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction (2021) — 29 citations
• Achieving Human Parity on Visual Question Answering (2021) — 24 citations
• IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling (2019) — 40 citations
• Incorporating External Knowledge into Machine Reading for Generative Question Answering (2019) — 46 citations