Guoyin Wang

Artificial Intelligence · with Carin · 2017–2023

A researcher in the Science corpus: 11 papers; trusts 428 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 252 citations into it; #14,110 of 35,345 by trust here, #9,170 by citations
• Computer Vision and Pattern Recognition — 166 citations into it; #11,363 of 21,575 by trust here, #10,500 by citations
• Natural Language Processing — 153 citations into it; #3,952 of 5,688 by trust here, #2,660 by citations
• Generative Models — 85 citations into it; #1,475 of 1,956 by trust here, #1,601 by citations
• Model Architectures — 41 citations into it
• Representation & Self-Supervised Learning — 41 citations into it; #2,484 of 3,145 by trust here, #1,671 by citations
• Optimization & Training — 25 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Guoyin Wang trusts overall

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

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

Who trusts Guoyin Wang overall

1. Chunyuan Li
2. Dinghan Shen
3. Yibo Yang
4. Nikhil Mehta
5. Lawrence Carin
6. Jiaxin Huang
7. Alessio Miaschi
8. Paris Perdikaris
9. Yizhe Zhang

Papers from Guoyin Wang's perspective

Ordered by how strongly the researchers Guoyin Wang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Generative Adversarial Network Training is a Continual Learning Problem (2018) — 53 citations
• Deconvolutional Paragraph Representation Learning (2017) — 79 citations
• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (2018) — 44 citations
• Adversarial Learning of a Sampler Based on an Unnormalized Distribution (2019) — 10 citations
• Joint Embedding of Words and Labels for Text Classification (2018) — 428 citations
• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018) — 351 citations
• GPT-NER: Named Entity Recognition via Large Language Models (2023) — 420 citations
• Complete Random Forest Based Class Noise Filtering Learning for Improving the Generalizability of Classifiers (2019) — 76 citations
• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018) — 64 citations
• Learning to Sample with Adversarially Learned Likelihood-Ratio (2018) — 14 citations
• On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018) — 9 citations