Jan Gasthaus

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 12 papers; trusts 116 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #14,899 of 35,345 by trust here, #12,974 by citations
• Time-Series & Forecasting — 92 citations into it; #18 of 381 by trust here, #19 by citations
• Management Science and Operations Research — 85 citations into it; #941 of 11,946 by trust here, #857 by citations
• Signal Processing — 76 citations into it; #658 of 7,572 by trust here, #671 by citations
• Computer Vision and Pattern Recognition — 68 citations into it
• Probabilistic & Bayesian Methods — 52 citations into it
• Statistics and Probability — 45 citations into it
• Model Architectures — 43 citations into it; #1,938 of 2,278 by trust here, #1,317 by citations

Whom Jan Gasthaus trusts overall

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

1. Rob J Hyndman
2. Alex Graves
3. Ilya Sutskever
4. Diederik P. Kingma
5. Valentin Flunkert
6. Geoffrey E. Hinton
7. Matthias W. Seeger
8. Yoshua Bengio
9. Oriol Vinyals
10. Tilmann Gneiting

Who trusts Jan Gasthaus overall

1. Bernie Wang
2. Bryan Lim
3. Hao Wang
4. Hansika Hewamalage
5. Tim Januschowski
6. Konstantinos Benidis
7. Yitian Chen
8. Danielle C. Maddix
9. Syama Sundar Rangapuram
10. Dit-Yan Yeung

Papers from Jan Gasthaus's perspective

Ordered by how strongly the researchers Jan Gasthaus 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017) — 3,009 citations
• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019) — 278 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
• Probabilistic Demand Forecasting at Scale (2017) — 158 citations
• Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017) — 20 citations