Radu Tudor Ionescu
Computer Vision and Pattern Recognition · 2013–2022
A researcher in the Science corpus: 9 papers; trusts 989 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 271 citations into it; #16,346 of 21,575 by trust here, #6,542 by citations
- Artificial Intelligence — 200 citations into it; #16,992 of 35,345 by trust here, #9,369 by citations
- Generative Models — 77 citations into it
- Model Architectures — 27 citations into it
- Optimization & Training — 24 citations into it
- Computer Networks and Communications — 20 citations into it
- Biomedical Engineering — 17 citations into it
- Media Technology — 14 citations into it
Whom Radu Tudor Ionescu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrea Vedaldi
- Andrew Zisserman
- Kaiming He
- David G. Lowe
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Karen Simonyan
- Yoshua Bengio
- Ross B. Girshick
Who trusts Radu Tudor Ionescu overall
- Guansong Pang
- Mariana-Iuliana Georgescu
- Shervin Minaee
- Paul Bodesheim
- Yu Tian
- Fahad Shahbaz Khan
- Hyunjong Park
- Chunhua Shen
- Wen Liu
- AmirAli Abdolrashidi
Papers from Radu Tudor Ionescu's perspective
Ordered by how strongly the researchers Radu Tudor Ionescu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Challenges in representation learning: A report on three machine learning contests (2013) — 2,020 citations
- Unmasking the Abnormal Events in Video (2017) — 310 citations
- Diffusion Models in Vision: A Survey (2022) — 2,213 citations
- Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (2020) — 355 citations
- Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection (2021) — 296 citations
- Curriculum Learning: A Survey (2021) — 577 citations
- Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video (2018) — 395 citations
- Objectness to improve the bag of visual words model (2014) — 25 citations
- Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition (2013)