Sarah Filippi

Artificial Intelligence · 2010–2016

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #16,908 of 35,345 by trust here, #20,377 by citations
• Statistical Learning Theory — 31 citations into it; #842 of 1,038 by trust here, #798 by citations
• Statistics and Probability — 31 citations into it; #8,780 of 12,932 by trust here, #6,552 by citations
• Reinforcement Learning — 14 citations into it
• Management Science and Operations Research — 11 citations into it; #7,189 of 11,946 by trust here, #9,383 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Sarah Filippi trusts overall

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

1. Arthur Gretton
2. Richard S. Sutton
3. Bernhard Schölkopf
4. Alex Smola
5. Martin L. Puterman
6. Christopher K. I. Williams
7. Eric R. Ziegel
8. Kenji Fukumizu
9. Michael Kearns
10. Francis R. Bach

Who trusts Sarah Filippi overall

1. R´emi Munos
2. Qingyun Wu
3. Aadirupa Saha
4. Lihong Li
5. Mohammad Sadegh Talebi
6. Tor Lattimore
7. Sébastien Bubeck
8. A. Carpentier
9. Olivier Capp'e
10. Hongning Wang

Papers from Sarah Filippi's perspective

Ordered by how strongly the researchers Sarah Filippi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Bayesian Learning of Kernel Embeddings (2016) — 48 citations
• Optimism in reinforcement learning and Kullback-Leibler divergence (2010) — 111 citations
• Large-scale kernel methods for independence testing (2016) — 144 citations
• Parametric Bandits: The Generalized Linear Case (2010) — 529 citations