Radu Marculescu

Computer Vision and Pattern Recognition · 2003–2024

A researcher in the Science corpus: 11 papers; trusts 508 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #17,666 of 21,575 by trust here, #17,757 by citations
• Computer Networks and Communications — 60 citations into it; #1,050 of 9,886 by trust here, #1,790 by citations
• Hardware and Architecture — 50 citations into it; #286 of 1,551 by trust here, #792 by citations
• Electrical and Electronic Engineering — 39 citations into it; #2,315 of 16,539 by trust here, #8,627 by citations
• Artificial Intelligence — 38 citations into it; #28,848 of 35,345 by trust here, #27,551 by citations
• Model Architectures — 24 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it
• Oncology — 13 citations into it

Whom Radu Marculescu trusts overall

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

1. William (Bill) J. Dally
2. Srinivasan Murali
3. Alex Krizhevsky
4. Jingcao Hu
5. Giovanni De Micheli
6. Geoffrey E. Hinton
7. Kaiming He
8. Brian Towles
9. Karen Simonyan
10. Yoshua Bengio

Who trusts Radu Marculescu overall

1. Ümit Y. Ogras
2. Srinivasan Murali
3. Ashutosh Kumar Singh
4. Amlan Ganguly
5. Chrysostomos Nicopoulos
6. Sujay Deb
7. Ming Li
8. K. Srinivasan
9. Md Mostafijur Rahman
10. Vincenzo Catania

Papers from Radu Marculescu's perspective

Ordered by how strongly the researchers Radu Marculescu 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Going deeper with convolutions
9. Long Short-Term Memory
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dream Distillation: A Data-Independent Model Compression Framework (2019) — 55 citations
• Electronic textiles: a platform for pervasive computing (2003) — 344 citations
• Energy-aware mapping for tile-based NoC architectures under performance constraints (2003) — 608 citations
• DyAD - smart routing for networks-on-chip (2004) — 586 citations
• "It's a small world after all": NoC performance optimization via long-range link insertion (2006) — 433 citations
• Outstanding Research Problems in NoC Design: System, Microarchitecture, and Circuit Perspectives (2009) — 750 citations
• EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation (2024) — 329 citations
• Medical Image Segmentation via Cascaded Attention Decoding (2023) — 333 citations
• On-chip communication architecture exploration: A quantitative evaluation of point-to-point, bus, and network-on-chip approaches (2007) — 300 citations
• Energy- and performance-aware mapping for regular NoC architectures (2005) — 687 citations
• Key research problems in NoC design: a holistic perspective (2005) — 374 citations