Qinliang Su

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 278 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 182 citations into it; #17,223 of 35,345 by trust here, #12,083 by citations
• Natural Language Processing — 130 citations into it; #4,344 of 5,688 by trust here, #3,053 by citations
• Computer Vision and Pattern Recognition — 91 citations into it
• Model Architectures — 35 citations into it
• Representation & Self-Supervised Learning — 31 citations into it; #2,766 of 3,145 by trust here, #2,120 by citations
• Optimization & Training — 27 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it; #1,415 of 1,525 by trust here, #1,487 by citations
• Generative Models — 23 citations into it

Whom Qinliang Su trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Radford M. Neal
7. Yair Weiss
8. Yann LeCun
9. Ruslan Salakhutdinov
10. Brendan J. Frey

Who trusts Qinliang Su overall

1. Dinghan Shen
2. Jochen Gast
3. Stefan Roth
4. Chunyuan Li

Papers from Qinliang Su's perspective

Ordered by how strongly the researchers Qinliang Su 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. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling (2016) — 42 citations
• Deconvolutional Latent-Variable Model for Text Sequence Matching (2017) — 69 citations
• Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms (2018) — 351 citations
• On Convergence Conditions of Gaussian Belief Propagation (2015) — 84 citations
• Low-Resource Generation of Multi-hop Reasoning Questions (2020) — 15 citations
• NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018) — 64 citations
• On the Use of Word Embeddings Alone to Represent Natural Language Sequences (2018) — 9 citations
• Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (2016) — 12 citations
• Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement (2015) — 6 citations