Piotr Koniusz

Artificial Intelligence · with Harandi · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 420 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #27,305 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 80 citations into it
• Representation & Self-Supervised Learning — 40 citations into it; #1,051 of 3,145 by trust here, #1,741 by citations
• Meta & Continual Learning — 31 citations into it; #236 of 1,041 by trust here, #486 by citations
• Optimization & Training — 19 citations into it
• Model Architectures — 9 citations into it
• Reinforcement Learning — 8 citations into it

Whom Piotr Koniusz trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Ilya Sutskever
6. Yoshua Bengio
7. Yann LeCun
8. Ronald J. Williams
9. Max Welling
10. Ross B. Girshick

Who trusts Piotr Koniusz overall

1. Christian Simon
2. Zhen Wang
3. Da-Wei Zhou
4. Ali Cheraghian
5. Marco Toldo
6. Shipeng Yan
7. Yaoyao Liu
8. Arjun Ashok
9. Minsoo Kang
10. Jiangwei Xie

Papers from Piotr Koniusz's perspective

Ordered by how strongly the researchers Piotr Koniusz 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On Learning the Geodesic Path for Incremental Learning (2021) — 155 citations
• On Modulating the Gradient for Meta-learning (2020) — 57 citations
• Adaptive Subspaces for Few-Shot Learning (2020) — 425 citations