Slawek Smyl

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 83 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it
• Management Science and Operations Research — 54 citations into it; #1,520 of 11,946 by trust here, #4,430 by citations
• Signal Processing — 39 citations into it; #4,430 of 7,572 by trust here, #5,829 by citations
• Economics and Econometrics — 30 citations into it; #13,492 of 23,481 by trust here, #18,597 by citations
• Model Architectures — 27 citations into it; #623 of 2,278 by trust here, #1,693 by citations
• Time-Series & Forecasting — 21 citations into it; #52 of 381 by trust here, #146 by citations
• Statistics and Probability — 20 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Slawek Smyl trusts overall

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

1. Everette S. Gardner Jr.
2. James W. Taylor
3. Spyros Makridakis
4. Rob J Hyndman
5. Kaiming He
6. Geoffrey E. Hinton
7. Robert E. Schapire
8. Ronald J. Williams
9. X. Zhang
10. Yoshua Bengio

Who trusts Slawek Smyl overall

1. Bryan Lim
2. Kashif Rasul
3. Taesung Kim
4. Konstantinos Benidis
5. Stefan Zohren
6. Fotios Petropoulos
7. Hansika Hewamalage
8. Spyros Makridakis
9. Calvin Seward
10. Syama Sundar Rangapuram

Papers from Slawek Smyl's perspective

Ordered by how strongly the researchers Slawek Smyl 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2020) — 764 citations
• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017) — 428 citations
• Time-series Extreme Event Forecasting with Neural Networks at Uber (2017)
• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017) — 32 citations