Wenlin Wang

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 9 papers; trusts 429 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 237 citations into it; #19,473 of 35,345 by trust here, #9,369 by citations
• Computer Vision and Pattern Recognition — 138 citations into it; #19,776 of 21,575 by trust here, #15,162 by citations
• Natural Language Processing — 117 citations into it; #5,068 of 5,688 by trust here, #2,767 by citations
• Representation & Self-Supervised Learning — 49 citations into it; #2,710 of 3,145 by trust here, #1,754 by citations
• Safety, Robustness & Interpretability — 49 citations into it; #1,944 of 2,251 by trust here, #1,945 by citations
• Model Architectures — 38 citations into it
• Generative Models — 31 citations into it; #1,796 of 1,956 by trust here, #1,355 by citations
• Probabilistic & Bayesian Methods — 25 citations into it; #1,434 of 1,525 by trust here, #1,220 by citations

Whom Wenlin Wang trusts overall

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

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

Who trusts Wenlin Wang overall

1. Dinghan Shen
2. Chunyuan Li
3. Mert Bulent Sariyildiz
4. Huan Zhang
5. Jeremy M. Cohen
6. Guoyin Wang
7. Junbo Jake Zhao
8. Aoxue Li
9. Lawrence Carin
10. Mark Braverman

Papers from Wenlin Wang's perspective

Ordered by how strongly the researchers Wenlin 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Topic Compositional Neural Language Model (2017) — 84 citations
• Continuous-Time Flows for Efficient Inference and Density Estimation (2017) — 62 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
• Certified Adversarial Robustness with Additive Noise (2018) — 393 citations
• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018) — 64 citations
• Second-Order Adversarial Attack and Certifiable Robustness (2018) — 125 citations
• On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018) — 9 citations
• Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017) — 154 citations