Irina Rish

Artificial Intelligence · 2001–2022

A researcher in the Science corpus: 17 papers; trusts 849 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 736 citations into it; #5,343 of 35,345 by trust here, #2,613 by citations
• Computer Vision and Pattern Recognition — 317 citations into it; #5,404 of 21,575 by trust here, #2,536 by citations
• Reinforcement Learning — 229 citations into it
• Meta & Continual Learning — 217 citations into it; #186 of 1,041 by trust here, #46 by citations
• Representation & Self-Supervised Learning — 144 citations into it; #1,107 of 3,145 by trust here, #539 by citations
• Management Science and Operations Research — 98 citations into it
• Cognitive Neuroscience — 71 citations into it; #8,816 of 10,852 by trust here, #6,180 by citations
• Model Architectures — 69 citations into it

Whom Irina Rish trusts overall

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

1. Geoffrey E. Hinton
2. J. Urgen Schmidhuber
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Richard S. Sutton
7. Yann LeCun
8. Kaiming He
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Irina Rish overall

1. Davide Chicco
2. Anamika Dhillon
3. Indika P. Wickramasinghe
4. Ahmed Lamkanfi
5. Lars Kai Hansen
6. Harsha Gurulingappa
7. Fan Fang
8. Giuseppe Jurman
9. Timothée Lesort
10. Gyanendra Kumar Verma

Papers from Irina Rish's perspective

Ordered by how strongly the researchers Irina Rish 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018) — 934 citations
• Towards Continual Reinforcement Learning: A Review and Perspectives (2020) — 437 citations
• Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning (2020) — 68 citations
• Scaling the Number of Tasks in Continual Learning (2022) — 10 citations
• Continual Learning In Environments With Polynomial Mixing Times (2021) — 17 citations
• Continual Learning with Foundation Models: An Empirical Study of Latent Replay (2022) — 79 citations
• Understanding Continual Learning Settings with Data Distribution Drift Analysis (2021) — 62 citations
• Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization (2021) — 353 citations
• Continual Learning in Deep Networks: an Analysis of the Last Layer (2021) — 24 citations
• Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers (2021) — 12 citations
• Sequoia: A Software Framework to Unify Continual Learning Research (2021) — 25 citations
• Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning (2020) — 80 citations
• COVI White Paper (2020) — 30 citations
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks (2015) — 765 citations
• Critical event prediction for proactive management in large-scale computer clusters (2003) — 305 citations
• Accuracy vs. efficiency trade-offs in probabilistic diagnosis (2002) — 43 citations
• An empirical study of the naive Bayes classifier (2001) — 1,115 citations