Bhargav Kanagal

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #20,235 of 35,345 by trust here, #15,866 by citations
• Information Systems — 32 citations into it; #3,246 of 8,771 by trust here, #3,098 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Recommender Systems — 14 citations into it
• Management Science and Operations Research — 13 citations into it; #5,807 of 11,946 by trust here, #4,267 by citations
• Marketing — 9 citations into it
• Information Retrieval & Search — 6 citations into it

Whom Bhargav Kanagal trusts overall

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

1. Yehuda Koren
2. L. Eon Bottou
3. Steffen Rendle
4. Judea Pearl
5. Deepak K. Agarwal
6. Yann LeCun
7. Geoffrey E. Hinton
8. Christoph Freudenthaler
9. Yoshua Bengio
10. Kaiming He

Who trusts Bhargav Kanagal overall

1. Steffen Rendle
2. Xiangnan He
3. Christoph Freudenthaler
4. Jingyue Gao
5. Trapit Bansal
6. Julian McAuley

Papers from Bhargav Kanagal's perspective

Ordered by how strongly the researchers Bhargav Kanagal 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Generic Coordinate Descent Framework for Learning from Implicit Feedback (2016) — 233 citations
• Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior (2012) — 78 citations
• RealFormer: Transformer Likes Residual Attention (2020) — 138 citations
• Latent factor models with additive and hierarchically-smoothed user preferences (2013) — 39 citations