Wenliang Dai

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 8 papers; trusts 682 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 464 citations into it; #33,664 of 35,345 by trust here, #3,673 by citations
• Natural Language Processing — 287 citations into it
• Computer Vision and Pattern Recognition — 285 citations into it; #16,459 of 21,575 by trust here, #2,712 by citations
• Multimodal Learning — 143 citations into it; #900 of 1,211 by trust here, #370 by citations
• Evaluation & Benchmarks — 94 citations into it
• Large Language Models — 85 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Model Architectures — 26 citations into it

Whom Wenliang Dai 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. Diederik P. Kingma
6. Ilya Sutskever
7. Ross B. Girshick
8. Yann LeCun
9. Alec Radford
10. Shaoqing Ren

Who trusts Wenliang Dai overall

1. Yifan Li
2. Penghao Wu
3. Haotian Liu
4. Bo Li
5. Yanwei Li
6. F. Liu
7. Bo Zhao
8. Ziwei Ji
9. Anthony Meng Huat Tiong
10. Renrui Zhang

Papers from Wenliang Dai's perspective

Ordered by how strongly the researchers Wenliang Dai 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

• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning (2023) — 3,705 citations
• Survey of Hallucination in Natural Language Generation (2022) — 5,175 citations
• Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022) — 113 citations
• NVLM: Open Frontier-Class Multimodal LLMs (2024)
• Visual Instruction Tuning with Polite Flamingo (2023) — 56 citations
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity (2023) — 1,776 citations
• Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2022) — 85 citations
• Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization (2021) — 85 citations