Himanshu Jain

Artificial Intelligence · International Conference on Learning Representations · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #21,688 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Information Systems — 14 citations into it
• Management Science and Operations Research — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Natural Language Processing — 5 citations into it

Whom Himanshu Jain trusts overall

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

1. Arkaitz Zubiaga
2. Harald Steck
3. Julian McAuley
4. Jason Aaron Edward Weston
5. Wei Bi
6. Bharath Hariharan
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Daniel J. Hsu
10. Paul R. Rosenbaum

Who trusts Himanshu Jain overall

1. Zhen Wang
2. Tong Wei
3. Noveen Sachdeva
4. Ankit Singh Rawat
5. Ronghui You
6. Michalis K. Titsias
7. Liu Liu
8. Carole-Jean Wu
9. Weiwei Liu
10. Dacheng Tao

Papers from Himanshu Jain's perspective

Ordered by how strongly the researchers Himanshu 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
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

• Long-tail learning via logit adjustment (2020) — 1,020 citations
• Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016) — 357 citations
• Sparse Local Embeddings for Extreme Multi-label Classification (2015) — 464 citations