Valentin Flunkert

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 51 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it
• Time-Series & Forecasting — 70 citations into it; #30 of 381 by trust here, #27 by citations
• Management Science and Operations Research — 67 citations into it; #1,252 of 11,946 by trust here, #1,252 by citations
• Signal Processing — 57 citations into it; #852 of 7,572 by trust here, #933 by citations
• Computer Vision and Pattern Recognition — 46 citations into it
• Statistics and Probability — 37 citations into it
• Model Architectures — 36 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it; #778 of 1,525 by trust here, #832 by citations

Whom Valentin Flunkert trusts overall

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

1. Rob J Hyndman
2. Nicolas Chapados
3. Geoffrey E. Hinton
4. Alex Graves
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ralph D. Snyder
8. Ilya Sutskever
9. Yann LeCun
10. Matthias W. Seeger

Who trusts Valentin Flunkert overall

1. Jan Gasthaus
2. Bernie Wang
3. Bryan Lim
4. Tim Januschowski
5. Dhruv Madeka
6. Abhimanyu Das
7. Syama Sundar Rangapuram
8. Haixu Wu
9. Danielle C. Maddix
10. Hao Wang

Papers from Valentin Flunkert's perspective

Ordered by how strongly the researchers Valentin Flunkert 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. ImageNet classification with deep convolutional neural networks
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. 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
• GluonTS: Probabilistic Time Series Models in Python (2019) — 131 citations
• Forecasting Big Time Series: Theory and Practice (2019) — 38 citations
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — 391 citations
• Probabilistic Forecasting with Spline Quantile Function RNNs (2019) — 201 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
• Bayesian Intermittent Demand Forecasting for Large Inventories (2016) — 103 citations