Valero Laparra
Computer Vision and Pattern Recognition · 2010–2017
A researcher in the Science corpus: 8 papers; trusts 110 researchers and is trusted by 106 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 66 citations into it; #3,176 of 21,575 by trust here, #5,082 by citations
- Artificial Intelligence — 32 citations into it; #10,134 of 35,345 by trust here, #17,412 by citations
- Cognitive Neuroscience — 27 citations into it; #2,098 of 10,852 by trust here, #3,702 by citations
- Signal Processing — 24 citations into it; #1,181 of 7,572 by trust here, #1,958 by citations
- Media Technology — 20 citations into it; #2,344 of 3,621 by trust here, #2,598 by citations
- Neuroscience — 19 citations into it
- Representation & Self-Supervised Learning — 19 citations into it
- Evaluation & Benchmarks — 17 citations into it
Whom Valero Laparra trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eero P. Simoncelli
- Aapo Hyvärinen
- Siwei Lyu
- Diederik P. Kingma
- Zhou Wang
- Jesus Malo
- Javier Portilla
- David J. Heeger
- Geoffrey E. Hinton
- Yann LeCun
Who trusts Valero Laparra overall
- Johannes Ballé
- Eirikur Agustsson
- David C. Minnen
- Fabian Mentzer
- David Lopez-Paz
- Ahmad Salman
- Michael Tschannen
- Lucas Theis
- Eero P. Simoncelli
- Kede Ma
Papers from Valero Laparra's perspective
Ordered by how strongly the researchers Valero Laparra 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
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- End-to-end Optimized Image Compression (2016) — 2,121 citations
- Eigen-Distortions of Hierarchical Representations (2017) — 85 citations
- End-to-end optimization of nonlinear transform codes for perceptual quality (2016) — 273 citations
- Density Modeling of Images using a Generalized Normalization Transformation (2015) — 446 citations
- Iterative Gaussianization: From ICA to Random Rotations (2011) — 144 citations
- Perceptual image quality assessment using a normalized Laplacian pyramid (2016) — 177 citations
- Fair Kernel Learning (2017) — 97 citations
- Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images (2010) — 66 citations