Tim Januschowski

Management Science and Operations Research · 2017–2020

A researcher in the Science corpus: 11 papers; trusts 69 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 263 citations into it; #11,634 of 11,946 by trust here, #4,267 by citations
• Artificial Intelligence — 195 citations into it; #20,464 of 35,345 by trust here, #15,542 by citations
• Statistics and Probability — 120 citations into it
• Signal Processing — 109 citations into it; #4,163 of 7,572 by trust here, #2,184 by citations
• Time-Series & Forecasting — 105 citations into it; #105 of 381 by trust here, #39 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Probabilistic & Bayesian Methods — 53 citations into it
• Model Architectures — 41 citations into it; #1,937 of 2,278 by trust here, #1,317 by citations

Whom Tim Januschowski trusts overall

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

1. Ilya Sutskever
2. Valentin Flunkert
3. David Salinas
4. Matthias W. Seeger
5. Rob J Hyndman
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Jan Gasthaus
9. Oriol Vinyals
10. Yoshua Bengio

Who trusts Tim Januschowski overall

1. Bernie Wang
2. Bryan Lim
3. Lijun Sun
4. Hao Wang
5. Jan Gasthaus
6. Konstantinos Benidis

Papers from Tim Januschowski's perspective

Ordered by how strongly the researchers Tim Januschowski 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet classification with deep convolutional neural networks
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• 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)
• Forecasting: theory and practice (2020) — 551 citations
• 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