Pier Luigi Dragotti
Computer Vision and Pattern Recognition · 2000–2020
A researcher in the Science corpus: 6 papers; trusts 20 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 44 citations into it; #10,979 of 21,575 by trust here, #13,815 by citations
- Computational Mechanics — 13 citations into it; #4,393 of 10,001 by trust here, #4,178 by citations
- Media Technology — 12 citations into it; #2,833 of 3,621 by trust here, #2,706 by citations
- Signal Processing — 8 citations into it; #5,488 of 7,572 by trust here, #4,750 by citations
- Optimization & Training — 7 citations into it
- Applied Mathematics — 6 citations into it; #4,492 of 7,620 by trust here, #4,234 by citations
- Evaluation & Benchmarks — 5 citations into it
Whom Pier Luigi Dragotti trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Michael A. Unser
- Martin Vetterli
- E.J. Candes
- David L. Donoho
- Ingrid Daubechies
- Stéphane Mallat
- Robert M. Gray
- Minh N. Do
- Justin K. Romberg
- Martin J. Wainwright
Who trusts Pier Luigi Dragotti overall
- Marco F. Duarte
- Yonina C. Eldar
- Ronen Tur
- Richard Baraniuk
- Thierry Blu
- Barbara Penna
- Niall P. Hurley
- E.J. Candes
- Badri Narayan Bhaskar
- Tammam Tillo
Papers from Pier Luigi Dragotti's perspective
Ordered by how strongly the researchers Pier Luigi Dragotti trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
Their papers
- Sparse Sampling of Signal Innovations (2008) — 455 citations
- Neural heterogeneity promotes robust learning (2020) — 298 citations
- DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018) — 985 citations
- Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix (2007) — 468 citations
- Directionlets: anisotropic multidirectional representation with separable filtering (2006) — 342 citations
- Compression of multispectral images by three-dimensional SPIHT algorithm (2000) — 315 citations