David Salinas

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 77 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #27,453 of 35,345 by trust here, #17,412 by citations
• Time-Series & Forecasting — 76 citations into it; #25 of 381 by trust here, #26 by citations
• Management Science and Operations Research — 75 citations into it; #1,088 of 11,946 by trust here, #1,061 by citations
• Signal Processing — 63 citations into it; #771 of 7,572 by trust here, #829 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Statistics and Probability — 45 citations into it
• Model Architectures — 40 citations into it
• Probabilistic & Bayesian Methods — 31 citations into it; #777 of 1,525 by trust here, #832 by citations

Whom David Salinas trusts overall

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

1. Alex Graves
2. Rob J Hyndman
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Ruofeng Wen
7. Yoshua Bengio
8. Diederik P. Kingma
9. J. Urgen Schmidhuber
10. Matthias W. Seeger

Who trusts David Salinas overall

1. Jan Gasthaus
2. Bernie Wang
3. Tim Januschowski
4. Bryan Lim
5. Syama Sundar Rangapuram
6. Kashif Rasul
7. Juan Miguel Lopez Alcaraz
8. Danielle C. Maddix
9. Dhruv Madeka
10. Valentin Flunkert

Papers from David Salinas's perspective

Ordered by how strongly the researchers David Salinas 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• 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 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