Doyen Sahoo

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 11 papers; trusts 1,551 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 387 citations into it; #28,493 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 319 citations into it
• Signal Processing — 89 citations into it; #7,548 of 7,572 by trust here, #3,309 by citations
• Management Science and Operations Research — 85 citations into it; #9,453 of 11,946 by trust here, #1,904 by citations
• Model Architectures — 69 citations into it
• Optimization & Training — 66 citations into it
• Representation & Self-Supervised Learning — 51 citations into it
• Information Systems — 49 citations into it

Whom Doyen Sahoo trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Diederik P. Kingma
6. Yann LeCun
7. Yoshua Bengio
8. Ilya Sutskever
9. Christian Szegedy
10. David G. Lowe

Who trusts Doyen Sahoo overall

1. Quang Pham
2. Yang Yang
3. Hung Le
4. Steven C. H. Hoi
5. Anusha Nagabandi
6. Shibhansh Dohare
7. Chenghao Liu
8. Vadim Borisov
9. Rahaf Aljundi
10. Da-Wei Zhou

Papers from Doyen Sahoo's perspective

Ordered by how strongly the researchers Doyen Sahoo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (2019) — 123 citations
• Online Deep Learning: Learning Deep Neural Networks on the Fly (2017) — 363 citations
• Online Learning: A Comprehensive Survey (2018) — 832 citations
• ETSformer: Exponential Smoothing Transformers for Time-series Forecasting (2022) — 321 citations
• CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (2022) — 483 citations
• RLHF Workflow: From Reward Modeling to Online RLHF (2024)
• Unified Training of Universal Time Series Forecasting Transformers (2024) — 656 citations
• Contextual Transformation Networks for Online Continual Learning (2021) — 61 citations
• Recent Advances in Deep Learning for Object Detection (2019) — 947 citations
• URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection (2018) — 325 citations
• Malicious URL Detection using Machine Learning: A Survey (2017) — 405 citations