Bartłomiej Twardowski

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 8 papers; trusts 984 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 347 citations into it; #14,750 of 35,345 by trust here, #4,956 by citations
• Computer Vision and Pattern Recognition — 306 citations into it; #10,045 of 21,575 by trust here, #3,810 by citations
• Meta & Continual Learning — 166 citations into it; #464 of 1,041 by trust here, #108 by citations
• Representation & Self-Supervised Learning — 116 citations into it
• Optimization & Training — 42 citations into it
• Radiology, Nuclear Medicine and Imaging — 32 citations into it; #3,186 of 3,265 by trust here, #990 by citations
• Model Architectures — 31 citations into it
• Efficiency & Compression — 28 citations into it

Whom Bartłomiej Twardowski 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. Yoshua Bengio
6. Yann LeCun
7. Ross B. Girshick
8. Karen Simonyan
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Bartłomiej Twardowski overall

1. Da-Wei Zhou
2. Hanbin Zhao
3. Marc Masana
4. Joost van de Weijer
5. Han-Jia Ye
6. Dipam Goswami
7. Xialei Liu
8. Liyuan Wang
9. Mingfu Liang
10. De-chuan Zhan

Papers from Bartłomiej Twardowski's perspective

Ordered by how strongly the researchers Bartłomiej Twardowski 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Semantic Drift Compensation for Class-Incremental Learning (2020) — 422 citations
• Class-Incremental Learning: Survey and Performance Evaluation on Image Classification (2020) — 891 citations
• Continually Learning Self-Supervised Representations with Projected Functional Regularization (2021) — 58 citations
• Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models (2025)
• Divide and not forget: Ensemble of selectively trained experts in Continual Learning (2024) — 59 citations
• Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning (2024) — 48 citations
• On Class Orderings for Incremental Learning (2020) — 17 citations
• RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning (2020) — 54 citations