Shivani Agarwal
Artificial Intelligence · 2002–2015
A researcher in the Science corpus: 7 papers; trusts 76 researchers and is trusted by 2,714 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 52 citations into it; #3,632 of 35,345 by trust here, #10,798 by citations
- Optimization & Training — 27 citations into it; #1,653 of 2,601 by trust here, #2,259 by citations
- Evaluation & Benchmarks — 22 citations into it; #1,647 of 3,147 by trust here, #3,004 by citations
- Computer Vision and Pattern Recognition — 19 citations into it; #741 of 21,575 by trust here, #4,988 by citations
- Information Retrieval & Search — 19 citations into it; #961 of 1,022 by trust here, #1,002 by citations
- Statistical Learning Theory — 14 citations into it; #1,000 of 1,038 by trust here, #985 by citations
- Statistics and Probability — 14 citations into it
- Computational Mechanics — 5 citations into it; #4,755 of 10,001 by trust here, #8,566 by citations
Whom Shivani Agarwal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark D. Reid
- Peter L. Bartlett
- Tong Zhang
- Henry Schneiderman
- Robert C. Williamson
- Corinna Cortes
- Michael I. Jordan
- Markus Weber
- Cynthia Rudin
- Clément Calauzènes
Who trusts Shivani Agarwal overall
- Bastian Leibe
- Andrew Zisserman
- Andreas Opelt
- Miquel Perelló Nieto
- Piotr Doll'ar
- Subhransu Maji
- Bernt Schiele
- Till Quack
- Kristen Grauman
- Jitendra Malik
Papers from Shivani Agarwal's perspective
Ordered by how strongly the researchers Shivani Agarwal trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning to detect objects in images via a sparse, part-based representation (2004) — 1,001 citations
- Learning a Sparse Representation for Object Detection (2002) — 673 citations
- Consistent Multiclass Algorithms for Complex Performance Measures (2015) — 68 citations
- Convex Calibration Dimension for Multiclass Loss Matrices (2014) — 48 citations
- Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses (2013) — 34 citations
- Surrogate regret bounds for bipartite ranking via strongly proper losses (2012) — 68 citations
- A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification (2012) — 145 citations