Oleksiy Ostapenko

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 489 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 335 citations into it; #9,732 of 35,345 by trust here, #6,103 by citations
• Computer Vision and Pattern Recognition — 222 citations into it; #6,826 of 21,575 by trust here, #4,502 by citations
• Meta & Continual Learning — 142 citations into it; #266 of 1,041 by trust here, #124 by citations
• Representation & Self-Supervised Learning — 104 citations into it
• Reinforcement Learning — 74 citations into it
• Model Architectures — 38 citations into it
• Optimization & Training — 35 citations into it
• Evaluation & Benchmarks — 29 citations into it

Whom Oleksiy Ostapenko trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Alex Graves
10. Ross B. Girshick

Who trusts Oleksiy Ostapenko overall

1. Gyuhak Kim
2. Ali Ayub
3. Tyler L. Hayes
4. Yiduo Guo
5. Enrico Fini
6. Alan R. Wagner
7. Liyuan Wang
8. Massimo Caccia
9. Changnan Xiao
10. Da-Wei Zhou

Papers from Oleksiy Ostapenko's perspective

Ordered by how strongly the researchers Oleksiy Ostapenko 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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 Remember: A Synaptic Plasticity Driven Framework for Continual Learning (2019) — 356 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 via Local Module Composition (2021) — 93 citations
• Continual Learning with Foundation Models: An Empirical Study of Latent Replay (2022) — 79 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
• Learning to remember: Dynamic Generative Memory for Continual Learning (2018)