Michael Bohlke-Schneider

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it
• Signal Processing — 56 citations into it
• Management Science and Operations Research — 48 citations into it
• Time-Series & Forecasting — 48 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Model Architectures — 31 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Statistics and Probability — 22 citations into it

Whom Michael Bohlke-Schneider trusts overall

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

1. Alex Graves
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Ruofeng Wen
5. Sepp Hochreiter
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yann LeCun
9. J. Urgen Schmidhuber
10. Alex Krizhevsky

Who trusts Michael Bohlke-Schneider overall

1. Juan Miguel Lopez Alcaraz
2. Tim Januschowski
3. Y. Tashiro
4. Kashif Rasul

Papers from Michael Bohlke-Schneider's perspective

Ordered by how strongly the researchers Michael Bohlke-Schneider 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. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019) — 278 citations
• GluonTS: Probabilistic Time Series Models in Python (2019) — 131 citations
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — 391 citations
• Chronos: Learning the Language of Time Series (2024) — 878 citations