Bernie Wang

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 11 papers; trusts 83 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #14,920 of 35,345 by trust here, #16,771 by citations
• Time-Series & Forecasting — 87 citations into it; #132 of 381 by trust here, #50 by citations
• Signal Processing — 82 citations into it; #2,880 of 7,572 by trust here, #2,400 by citations
• Management Science and Operations Research — 78 citations into it; #11,633 of 11,946 by trust here, #4,430 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #15,158 of 21,575 by trust here, #18,782 by citations
• Probabilistic & Bayesian Methods — 55 citations into it
• Model Architectures — 37 citations into it; #2,137 of 2,278 by trust here, #1,317 by citations
• Generative Models — 35 citations into it

Whom Bernie Wang trusts overall

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

1. Diederik P. Kingma
2. Matthias W. Seeger
3. Geoffrey E. Hinton
4. Rob J Hyndman
5. Yoshua Bengio
6. Valentin Flunkert
7. Ilya Sutskever
8. Danilo Jimenez Rezende
9. Neil D. Lawrence
10. Yann LeCun

Who trusts Bernie Wang overall

1. Bryan Lim
2. Hao Wang
3. Tim Januschowski
4. Rajat Sen
5. Haixu Wu
6. Jan Gasthaus
7. Yitian Chen
8. Dit-Yan Yeung
9. Konstantinos Benidis
10. Hansika Hewamalage

Papers from Bernie Wang's perspective

Ordered by how strongly the researchers Bernie 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Factors with Gaussian Processes for Forecasting (2018) — 47 citations
• GluonTS: Probabilistic Time Series Models in Python (2019) — 131 citations
• Deep Factors for Forecasting (2019) — 205 citations
• Forecasting Big Time Series: Theory and Practice (2019) — 38 citations
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — 391 citations
• Forecasting Big Time Series: Old and New (2018) — 74 citations
• Deep Learning for Forecasting (2020)
• Probabilistic Forecasting with Spline Quantile Function RNNs (2019) — 201 citations
• Deep State Space Models for Time Series Forecasting (2018) — 902 citations
• Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017) — 20 citations
• Sparse Variational Inference for Generalized GP Models (2015) — 28 citations