Anastasia Pentina

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 6 papers; trusts 141 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #5,124 of 35,345 by trust here, #8,525 by citations
• Representation & Self-Supervised Learning — 49 citations into it
• Statistical Learning Theory — 28 citations into it; #355 of 1,038 by trust here, #354 by citations
• Computational Mechanics — 22 citations into it; #2,186 of 10,001 by trust here, #2,309 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Optimization & Training — 11 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Statistics and Probability — 7 citations into it

Whom Anastasia Pentina trusts overall

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

1. David A. McAllester
2. Jonathan Baxter
3. Andreas Maurer
4. Theodoros Evgeniou
5. J. Langford
6. Massimiliano Pontil
7. Paul Ruvolo
8. Tony Jebara
9. Andreas Argyriou
10. François Laviolette

Who trusts Anastasia Pentina overall

1. Giulia Denevi
2. Ron Amit
3. Rahaf Aljundi
4. Pierre Alquier
5. Christoph H. Lampert
6. Yu Zhang
7. Carlo Ciliberto
8. Qianru Sun
9. Massimiliano Pontil
10. Zhiyuan Chen

Papers from Anastasia Pentina's perspective

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

Their papers

• Curriculum learning of multiple tasks (2014) — 274 citations
• A PAC-Bayesian bound for Lifelong Learning (2013) — 222 citations
• Multi-task Learning with Labeled and Unlabeled Tasks (2016) — 42 citations
• Lifelong Learning with Weighted Majority Votes (2016) — 29 citations
• Multi-task and Lifelong Learning of Kernels (2015) — 64 citations
• Lifelong Learning with Non-i.i.d. Tasks (2015) — 106 citations