Rajat Sen

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 6 papers; trusts 43 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Management Science and Operations Research — 58 citations into it; #9,122 of 11,946 by trust here, #4,267 by citations
• Signal Processing — 38 citations into it; #5,868 of 7,572 by trust here, #2,525 by citations
• Time-Series & Forecasting — 18 citations into it; #317 of 381 by trust here, #153 by citations
• Model Architectures — 15 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Statistics and Probability — 11 citations into it

Whom Rajat Sen trusts overall

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

1. Kirthevasan Kandasamy
2. Ed McKenzie
3. L. Eon Bottou
4. Geoffrey E. Hinton
5. Marco Cuturi
6. Helmut Ltkepohl
7. Kyoung-jae Kim
8. Gautam Dasarathy
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Rajat Sen overall

1. Hansika Hewamalage
2. Tian Zhou
3. Abhimanyu Das
4. Pim de Haan
5. Xilu Wang
6. Karttikeya Mangalam
7. David Salinas
8. Xueyan Yin
9. Yuntao Du
10. Minhao Liu

Papers from Rajat Sen's perspective

Ordered by how strongly the researchers Rajat Sen 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Identifying Best Interventions through Online Importance Sampling (2017) — 82 citations
• Contextual Bandits with Latent Confounders: An NMF Approach (2016) — 50 citations
• A decoder-only foundation model for time-series forecasting (2023) — 785 citations
• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019) — 473 citations
• Long-term Forecasting with TiDE: Time-series Dense Encoder (2023) — 600 citations
• Multi-Fidelity Black-Box Optimization with Hierarchical Partitions (2018) — 58 citations