Sagar Jain

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 102 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #34,561 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Information Systems — 62 citations into it; #8,512 of 8,771 by trust here, #2,912 by citations
• Management Science and Operations Research — 40 citations into it
• Model Architectures — 36 citations into it
• Reinforcement Learning — 31 citations into it
• Recommender Systems — 25 citations into it
• Optimization & Training — 22 citations into it

Whom Sagar Jain trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ronald J. Williams
5. Alex Krizhevsky
6. Ilya Sutskever
7. Oriol Vinyals
8. Thorsten Joachims
9. Kaiming He
10. Yann LeCun

Who trusts Sagar Jain overall

1. Yu. V. Gorishniy

Papers from Sagar Jain's perspective

Ordered by how strongly the researchers Sagar Jain 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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Top-K Off-Policy Correction for a REINFORCE Recommender System (2018) — 558 citations
• Seq2Slate: Re-ranking and Slate Optimization with RNNs (2018) — 114 citations
• Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018) — 371 citations
• DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems (2020) — 865 citations
• Understanding and Improving Knowledge Distillation (2020) — 164 citations