Liang Wang
Artificial Intelligence · arXiv.org · 2018–2024
A researcher in the Science corpus: 8 papers; trusts 96 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 194 citations into it; #25,587 of 35,345 by trust here, #3,816 by citations
- Natural Language Processing — 117 citations into it
- Computer Vision and Pattern Recognition — 56 citations into it
- Information Retrieval & Search — 53 citations into it
- Evaluation & Benchmarks — 48 citations into it
- Representation & Self-Supervised Learning — 46 citations into it; #3,043 of 3,145 by trust here, #1,811 by citations
- Information Systems — 17 citations into it
- Model Architectures — 13 citations into it
Whom Liang Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Kaiming He
- Tomas Mikolov
- Yann LeCun
- Richard Socher
- Jacob Devlin
Who trusts Liang Wang overall
- Hong-Liu Cao
- Luke Merrick
- Zach Nussbaum
- Max Vargas
- Jean Kaddour
- Bowen Zhang
- Kai Sun
- Rajkumar Pujari
- Hongming Zhang
- Dawei Zhu
Papers from Liang Wang's perspective
Ordered by how strongly the researchers Liang Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems (2020) — 96 citations
- Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension (2018) — 78 citations
- Text Embeddings by Weakly-Supervised Contrastive Pre-training (2022) — 1,460 citations
- Fine-Tuning LLaMA for Multi-Stage Text Retrieval (2023) — 431 citations
- Multilingual E5 Text Embeddings: A Technical Report (2024) — 521 citations
- LongEmbed: Extending Embedding Models for Long Context Retrieval (2024)
- Generative Representational Instruction Tuning (2024)
- Improving Text Embeddings with Large Language Models (2023) — 400 citations