Priyam Chatterjee
Computer Vision and Pattern Recognition · 2009–2019
A researcher in the Science corpus: 5 papers; trusts 119 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 99 citations into it; #4,958 of 21,575 by trust here, #7,938 by citations
- Media Technology — 37 citations into it; #659 of 3,621 by trust here, #1,206 by citations
- Artificial Intelligence — 27 citations into it
- Computational Mechanics — 25 citations into it; #4,525 of 10,001 by trust here, #4,450 by citations
- Model Architectures — 12 citations into it
- Statistics and Probability — 12 citations into it
- Signal Processing — 9 citations into it
Whom Priyam Chatterjee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bradley Efron
- Charles Kervrann
- Michael Elad
- Michal Aharon
- Keinosuke Fukunaga
- Jérôme Boulanger
- Sathish Ramani
- Julien Mairal
- Geoffrey E. Hinton
- Harshinder Singh
Who trusts Priyam Chatterjee overall
- Marc Lebrun
- Weisheng Dong
- Yaniv Romano
- Peyman Milanfar
- Anat Levin
- Xiao-Jiao Mao
- Julien Mairal
- Lei Zhang
- Ling Shao
- Xinyuan Zhang
Papers from Priyam Chatterjee's perspective
Ordered by how strongly the researchers Priyam Chatterjee 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)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Is Denoising Dead? (2010) — 475 citations
- Efficient Segmentation: Learning Downsampling Near Semantic Boundaries (2019) — 109 citations
- Patch-Based Near-Optimal Image Denoising (2012) — 328 citations
- Clustering-Based Denoising With Locally Learned Dictionaries (2009) — 329 citations
- Practical Bounds on Image Denoising: From Estimation to Information (2011) — 59 citations