Clément Calauzènes

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it
• Information Systems — 17 citations into it
• Statistics and Probability — 13 citations into it
• Information Retrieval & Search — 10 citations into it; #702 of 1,022 by trust here, #821 by citations
• Optimization & Training — 10 citations into it; #1,497 of 2,601 by trust here, #2,162 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Reinforcement Learning — 8 citations into it

Whom Clément Calauzènes trusts overall

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

1. Olivier Chapelle
2. Tong Zhang
3. Ingo Steinwart
4. Thorsten Joachims
5. W. Kotłowski
6. Michael J. Taylor
7. John Guiver
8. David Cossock
9. Francis R. Bach
10. Krzysztof Dembczynski

Who trusts Clément Calauzènes overall

1. Harish Guruprasad Ramaswamy
2. Shivani Agarwal
3. Kirill Struminsky
4. Anton Osokin
5. Simon Lacoste-Julien
6. Ambuj Tewari
7. Yining Wang
8. Harikrishna Narasimhan
9. Sergey Levine

Papers from Clément Calauzènes's perspective

Ordered by how strongly the researchers Clément Calauzènes 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Offline A/B Testing for Recommender Systems (2018) — 256 citations
• Improved Optimistic Algorithms for Logistic Bandits (2020) — 140 citations
• "On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking" (2012) — 52 citations
• Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision (2011) — 38 citations