Wei-Hung Weng

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 292 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #15,823 of 35,345 by trust here, #4,979 by citations
• Computer Vision and Pattern Recognition — 75 citations into it
• Natural Language Processing — 69 citations into it; #5,405 of 5,688 by trust here, #1,910 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #2,502 of 3,145 by trust here, #1,044 by citations
• Evaluation & Benchmarks — 23 citations into it
• Signal Processing — 19 citations into it
• Model Architectures — 17 citations into it
• Speech & Audio — 15 citations into it; #937 of 1,076 by trust here, #783 by citations

Whom Wei-Hung Weng trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Kaiming He
7. Yann LeCun
8. Alex Graves
9. Tomas Mikolov
10. Jimmy Ba

Who trusts Wei-Hung Weng overall

1. Chao Yu
2. Wei Zhang
3. Chun-Hao Kingsley Chang
4. Jun Shu
5. Jiming Liu
6. Michihiro Yasunaga
7. Xiaofeng He
8. Mingjie Mai
9. Jan Chorowski
10. Yi-Chen Chen

Papers from Wei-Hung Weng's perspective

Ordered by how strongly the researchers Wei-Hung Weng trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces (2018) — 108 citations
• Publicly Available Clinical BERT Embeddings (2019) — 2,728 citations
• What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams (2020) — 1,893 citations
• Clinically Accurate Chest X-Ray Report Generation (2019) — 353 citations
• Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021) — 406 citations
• Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval (2017) — 70 citations
• Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients (2017) — 60 citations