Ivan W. Selesnick
Computer Vision and Pattern Recognition · 2001–2021
A researcher in the Science corpus: 12 papers; trusts 65 researchers and is trusted by 56 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 50 citations into it; #3,394 of 21,575 by trust here, #5,422 by citations
- Computational Mechanics — 21 citations into it; #4,064 of 10,001 by trust here, #7,884 by citations
- Applied Mathematics — 19 citations into it; #6,555 of 7,620 by trust here, #5,850 by citations
- Media Technology — 18 citations into it; #313 of 3,621 by trust here, #575 by citations
- Signal Processing — 16 citations into it; #812 of 7,572 by trust here, #1,433 by citations
- Statistics and Probability — 11 citations into it
- Optimization & Training — 8 citations into it
- Analytical Chemistry — 7 citations into it; #174 of 1,254 by trust here, #741 by citations
Whom Ivan W. Selesnick trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- N. Kingsbury
- Ingrid Daubechies
- David L. Donoho
- Charles K. Chui
- William T. Freeman
- Mário A. T. Figueiredo
- Scott Shaobing Chen
- Alexander Petukhov
- Wenjie He
- E.J. Candes
Who trusts Ivan W. Selesnick overall
- Tiantian Guo
- Thierry Blu
- Florian Luisier
- Tongpo Zhang
- Richard Baraniuk
- Shen-En Qian
- Enggee Lim
- Laurent Demanet
- L. Aguiar
- Vladimir Katkovnik
Papers from Ivan W. Selesnick's perspective
Ordered by how strongly the researchers Ivan W. Selesnick 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)
- Regression Shrinkage and Selection via the Lasso
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Atomic Decomposition by Basis Pursuit
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
Their papers
- The dual-tree complex wavelet transform (2005) — 2,598 citations
- Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency (2002) — 1,082 citations
- Solving the optimal PWM problem for single-phase inverters (2002) — 102 citations
- Effects of hippocampal interictal discharge timing, duration, and spatial extent on list learning (2021)
- Sparse Regularization via Convex Analysis (2017) — 389 citations
- Chromatogram baseline estimation and denoising using sparsity (BEADS) (2014) — 203 citations
- Wavelet Transform With Tunable Q-Factor (2011) — 611 citations
- The double-density dual-tree DWT (2004) — 320 citations
- Symmetric wavelet tight frames with two generators (2004) — 132 citations
- The design of approximate Hilbert transform pairs of wavelet bases (2002) — 320 citations
- Bivariate shrinkage with local variance estimation (2002) — 656 citations
- Hilbert transform pairs of wavelet bases (2001) — 374 citations