Wei Wang

Artificial Intelligence · 19 papers · 2017–2023

A researcher in the Science corpus: 19 papers; trusts 832 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 588 citations into it; #8,161 of 35,345 by trust here, #2,914 by citations
• Computer Vision and Pattern Recognition — 357 citations into it; #9,635 of 21,575 by trust here, #5,333 by citations
• Natural Language Processing — 293 citations into it; #2,403 of 5,688 by trust here, #1,418 by citations
• Large Language Models — 144 citations into it
• Evaluation & Benchmarks — 101 citations into it
• Model Architectures — 71 citations into it
• Representation & Self-Supervised Learning — 61 citations into it; #2,658 of 3,145 by trust here, #1,658 by citations
• Information Systems — 37 citations into it; #7,915 of 8,771 by trust here, #2,471 by citations

Whom Wei Wang trusts overall

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

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

Who trusts Wei Wang overall

1. Chenliang Li
2. Ming Yan
3. Ming Ding
4. Minghao Hu
5. Reham Osama
6. Rajarshi Das
7. Bin Bi
8. Nina Poerner
9. Yiming Cui
10. Kai Sun

Papers from Wei Wang's perspective

Ordered by how strongly the researchers Wei 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Ask the Right Questions: Active Question Reformulation with Reinforcement Learning (2017) — 177 citations
• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019) — 301 citations
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022) — 295 citations
• Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (2018) — 178 citations
• Language-agnostic BERT Sentence Embedding (2020) — 1,305 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
• RRHF: Rank Responses to Align Language Models with Human Feedback without tears (2023) — 545 citations
• Qwen Technical Report (2023) — 4,095 citations
• PALM: Pre-training an Autoencoding&autoregressive Language Model for Context-conditioned Generation (2020) — 78 citations
• Understanding Architectures Learnt by Cell-based Neural Architecture Search (2019) — 93 citations
• How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2023) — 310 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
• VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation (2020) — 23 citations
• Efficient Memory Management for GPU-based Deep Learning Systems (2019) — 45 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