Mingxuan Wang

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 19 papers; trusts 515 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 430 citations into it; #26,206 of 35,345 by trust here, #6,569 by citations
• Natural Language Processing — 289 citations into it; #5,377 of 5,688 by trust here, #2,069 by citations
• Computer Vision and Pattern Recognition — 152 citations into it
• Speech & Audio — 105 citations into it
• Evaluation & Benchmarks — 66 citations into it
• Model Architectures — 65 citations into it
• Signal Processing — 59 citations into it
• Representation & Self-Supervised Learning — 58 citations into it

Whom Mingxuan Wang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Graves
6. Yann LeCun
7. Alex Krizhevsky
8. Dzmitry Bahdanau
9. Kaiming He
10. Kyunghyun Cho

Who trusts Mingxuan Wang overall

1. Jiuxiang Gu
2. Rohollah Soltani
3. Zhen-Le Yang
4. Biao Zhang
5. Hongshen Chen
6. Yoon Kim
7. Junyi Li
8. Amirsina Torfi
9. Angela Fan
10. Hui Jiang

Papers from Mingxuan Wang's perspective

Ordered by how strongly the researchers Mingxuan 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Encoding Source Language with Convolutional Neural Network for Machine Translation (2015) — 109 citations
• Deep Semantic Role Labeling with Self-Attention (2017) — 320 citations
• Towards Making the Most of BERT in Neural Machine Translation (2019) — 167 citations
• genCNN: A Convolutional Architecture for Word Sequence Prediction (2015) — 29 citations
• On the Sentence Embeddings from BERT for Semantic Textual Similarity (2020) — 254 citations
• Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information (2020) — 134 citations
• LightSeq: A High Performance Inference Library for Transformers (2021) — 72 citations
• Syntax-based Deep Matching of Short Texts (2015) — 87 citations
• PolyVoice: Language Models for Speech to Speech Translation (2023) — 35 citations
• DUB: Discrete Unit Back-translation for Speech Translation (2023) — 34 citations
• GigaST: A 10, 000-hour Pseudo Speech Translation Corpus (2022) — 30 citations
• Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation (2022) — 19 citations
• CoBERT: Self-Supervised Speech Representation Learning Through Code Representation Learning (2022) — 7 citations
• SEScore2: Retrieval Augmented Pretraining for Text Generation Evaluation (2022) — 5 citations
• End-to-end Speech Translation via Cross-modal Progressive Training (2021) — 81 citations
• Learning Shared Semantic Space for Speech-to-Text Translation (2021) — 90 citations
• Counter-Interference Adapter for Multilingual Machine Translation (2021) — 78 citations
• Finding Sparse Structures for Domain Specific Neural Machine Translation (2021) — 33 citations
• Deep Neural Machine Translation with Linear Associative Unit (2017) — 54 citations