Dawen Liang

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 8 papers; trusts 94 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #13,332 of 35,345 by trust here, #7,010 by citations
• Probabilistic & Bayesian Methods — 61 citations into it; #772 of 1,525 by trust here, #424 by citations
• Information Systems — 58 citations into it; #2,480 of 8,771 by trust here, #1,489 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #7,864 of 21,575 by trust here, #5,620 by citations
• Recommender Systems — 49 citations into it; #132 of 233 by trust here, #104 by citations
• Management Science and Operations Research — 32 citations into it; #11,566 of 11,946 by trust here, #5,316 by citations
• Signal Processing — 25 citations into it; #3,015 of 7,572 by trust here, #1,033 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #2,240 of 3,145 by trust here, #2,454 by citations

Whom Dawen Liang trusts overall

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

1. David M. Blei
2. Diederik P. Kingma
3. Steffen Rendle
4. Ruslan Salakhutdinov
5. Geoffrey E. Hinton
6. Radford M. Neal
7. Yehuda Koren
8. Yoshua Bengio
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Dawen Liang overall

1. Xiangnan He
2. Harald Steck
3. Xiang Wang
4. Wenjie Wang
5. Babak Esmaeili
6. Aminu Da’u
7. Fuli Feng
8. Shuai Zhang
9. Jun Xu
10. Guangneng Hu

Papers from Dawen Liang's perspective

Ordered by how strongly the researchers Dawen Liang 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Edward: A library for probabilistic modeling, inference, and criticism (2016) — 306 citations
• librosa: Audio and Music Signal Analysis in Python (2015) — 3,082 citations
• On the challenges of learning with inference networks on sparse, high-dimensional data (2017) — 90 citations
• Variational Autoencoders for Collaborative Filtering (2018) — 1,520 citations
• Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence (2016) — 277 citations
• Modeling User Exposure in Recommendation (2015) — 426 citations
• Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks (2015) — 73 citations
• MIR_EVAL: A Transparent Implementation of Common MIR Metrics (2014) — 639 citations