Polina Kirichenko
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 7 papers; trusts 627 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 243 citations into it; #27,192 of 35,345 by trust here, #14,438 by citations
- Computer Vision and Pattern Recognition — 187 citations into it; #16,700 of 21,575 by trust here, #10,677 by citations
- Generative Models — 54 citations into it
- Optimization & Training — 49 citations into it
- Representation & Self-Supervised Learning — 44 citations into it
- Probabilistic & Bayesian Methods — 42 citations into it
- Safety, Robustness & Interpretability — 40 citations into it
- Efficiency & Compression — 31 citations into it
Whom Polina Kirichenko trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Ilya Sutskever
- Radford M. Neal
- Christian Szegedy
- Karen Simonyan
Who trusts Polina Kirichenko overall
Papers from Polina Kirichenko's perspective
Ordered by how strongly the researchers Polina Kirichenko trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Semi-Supervised Learning with Normalizing Flows (2019) — 137 citations
- Why Normalizing Flows Fail to Detect Out-of-Distribution Data (2020) — 362 citations
- Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations (2022) — 483 citations
- Task-agnostic Continual Learning with Hybrid Probabilistic Models (2021) — 24 citations
- Subspace Inference for Bayesian Deep Learning (2019) — 164 citations
- Does Knowledge Distillation Really Work? (2021) — 292 citations
- SWALP : Stochastic Weight Averaging in Low-Precision Training (2019) — 107 citations