Boris N. Oreshkin

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 8 papers; trusts 907 researchers and is trusted by 349 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it; #2,897 of 35,345 by trust here, #4,797 by citations
• Computer Vision and Pattern Recognition — 120 citations into it; #2,234 of 21,575 by trust here, #3,732 by citations
• Representation & Self-Supervised Learning — 74 citations into it; #509 of 3,145 by trust here, #819 by citations
• Management Science and Operations Research — 40 citations into it; #2,667 of 11,946 by trust here, #1,904 by citations
• Time-Series & Forecasting — 32 citations into it; #152 of 381 by trust here, #73 by citations
• Signal Processing — 30 citations into it; #1,848 of 7,572 by trust here, #1,538 by citations
• Model Architectures — 28 citations into it
• Meta & Continual Learning — 22 citations into it; #730 of 1,041 by trust here, #583 by citations

Whom Boris N. Oreshkin trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Diederik P. Kingma
5. Yoshua Bengio
6. Karen Simonyan
7. Yann LeCun
8. Li Fei-Fei
9. Ilya Sutskever
10. Oriol Vinyals

Who trusts Boris N. Oreshkin overall

1. Han-Jia Ye
2. Haixu Wu
3. De-chuan Zhan
4. Chi Zhang
5. Qianru Sun
6. Hexiang Hu
7. Sung Whan Yoon
8. Yaoyao Liu
9. Wei-Lun Chao
10. Tian Zhou

Papers from Boris N. Oreshkin's perspective

Ordered by how strongly the researchers Boris N. Oreshkin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• TADAM: Task dependent adaptive metric for improved few-shot learning (2018) — 1,482 citations
• AMP: Adaptive Masked Proxies for Few-Shot Segmentation (2019) — 213 citations
• Deep Prior (2017) — 7 citations
• Uncertainty in Multitask Transfer Learning (2018) — 21 citations
• Adaptive Cross-Modal Few-Shot Learning (2019) — 339 citations
• Adaptive Masked Weight Imprinting for Few-Shot Segmentation (2019) — 35 citations
• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019) — 1,690 citations
• N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2022) — 471 citations