Jelena Luketina

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 6 papers; trusts 140 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #8,655 of 35,345 by trust here, #2,594 by citations
• Reinforcement Learning — 78 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it; #13,777 of 21,575 by trust here, #7,027 by citations
• Natural Language Processing — 61 citations into it
• Representation & Self-Supervised Learning — 27 citations into it; #1,424 of 3,145 by trust here, #448 by citations
• Optimization & Training — 26 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Control and Systems Engineering — 11 citations into it

Whom Jelena Luketina trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Ilya Sutskever
7. Richard S. Sutton
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Jelena Luketina overall

1. Jonathan Lorraine
2. George Philipp
3. Kacper Sokol
4. J'er'emy Scheurer
5. Yaoyao Liu
6. Zalán Borsos
7. Weiyu Cheng
8. Paul Vicol
9. Amirreza Shaban
10. Edward Grefenstette

Papers from Jelena Luketina's perspective

Ordered by how strongly the researchers Jelena Luketina 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Progress & Compress: A scalable framework for continual learning (2018) — 1,048 citations
• A Survey of Reinforcement Learning Informed by Natural Language (2019) — 313 citations
• Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters (2015) — 183 citations
• Transient Non-stationarity and Generalisation in Deep Reinforcement Learning (2020) — 106 citations
• Understanding the Effects of RLHF on LLM Generalisation and Diversity (2023) — 411 citations
• 33rd International Conference on Machine Learning, ICML 2016 (2016) — 10 citations