Yashoteja Prabhu

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #5,709 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #10,280 of 21,575 by trust here, #14,151 by citations
• Information Systems — 17 citations into it; #3,188 of 8,771 by trust here, #3,876 by citations
• Management Science and Operations Research — 11 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Computational Mechanics — 5 citations into it
• Signal Processing — 5 citations into it

Whom Yashoteja Prabhu trusts overall

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

1. Jason Aaron Edward Weston
2. Bharath Hariharan
3. Wei Bi
4. Daniel J. Hsu
5. Farbound Tai
6. Rong-En Fan
7. Ashish Kapoor
8. Geoffrey E. Hinton
9. Guo-Xun Yuan
10. Rahul Agrawal

Who trusts Yashoteja Prabhu overall

1. Wei-Cheng Chang
2. Kush Bhatia
3. Jinseok Nam
4. Wei Zhao
5. Wenjie Zhang
6. Wen Sun
7. Zhen Wang
8. Himanshu Jain
9. Yuexin Wu
10. Tong Wei

Papers from Yashoteja Prabhu's perspective

Ordered by how strongly the researchers Yashoteja Prabhu 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

• FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning (2014) — 423 citations
• Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013) — 306 citations
• Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016) — 357 citations
• HOBIT : Hardness Optimized Batch Sampling for InfoNCE Training