Syama Sundar Rangapuram

Artificial Intelligence · 2012–2024

A researcher in the Science corpus: 8 papers; trusts 56 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #14,515 of 35,345 by trust here, #22,463 by citations
• Signal Processing — 70 citations into it
• Time-Series & Forecasting — 68 citations into it; #346 of 381 by trust here, #79 by citations
• Management Science and Operations Research — 66 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it; #9,329 of 21,575 by trust here, #19,766 by citations
• Probabilistic & Bayesian Methods — 36 citations into it
• Model Architectures — 31 citations into it; #1,936 of 2,278 by trust here, #1,317 by citations
• Statistics and Probability — 27 citations into it

Whom Syama Sundar Rangapuram trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Rob J Hyndman
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Alex Graves
8. Alex Krizhevsky
9. Oriol Vinyals
10. Sepp Hochreiter

Who trusts Syama Sundar Rangapuram overall

1. Bryan Lim
2. Fabio Galasso
3. Yen-Chang Hsu
4. Rose Yu
5. Bernie Wang
6. Hao Wang
7. Anna Khoreva
8. Haixu Wu
9. Danielle C. Maddix
10. Hansika Hewamalage

Papers from Syama Sundar Rangapuram's perspective

Ordered by how strongly the researchers Syama Sundar Rangapuram 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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Constrained 1-Spectral Clustering (2012) — 89 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
• Deep Learning for Forecasting (2020)
• Probabilistic Forecasting with Spline Quantile Function RNNs (2019) — 201 citations
• Deep State Space Models for Time Series Forecasting (2018) — 902 citations
• Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale (2017) — 20 citations