Volkan Cevher
Computational Mechanics · 2008–2022
A researcher in the Science corpus: 15 papers; trusts 181 researchers and is trusted by 47 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 123 citations into it; #782 of 10,001 by trust here, #485 by citations
- Artificial Intelligence — 107 citations into it; #7,919 of 35,345 by trust here, #9,870 by citations
- Optimization & Training — 95 citations into it; #1,002 of 2,601 by trust here, #1,509 by citations
- Computer Vision and Pattern Recognition — 62 citations into it
- Computational Theory and Mathematics — 35 citations into it; #6,741 of 12,182 by trust here, #2,761 by citations
- Statistics and Probability — 34 citations into it; #7,350 of 12,932 by trust here, #9,714 by citations
- Biomedical Engineering — 32 citations into it
- Numerical Analysis — 28 citations into it; #1,878 of 3,586 by trust here, #1,968 by citations
Whom Volkan Cevher trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Patrick L. Combettes
- E.J. Candes
- Shai Shalev-Shwartz
- Stéphane Mallat
- Anatoli B. Juditsky
- Tong Zhang
- Martin Jaggi
- Paul Tseng
- Geoffrey E. Hinton
Who trusts Volkan Cevher overall
- Francis R. Bach
- Nicolas Loizou
- Sharan Vaswani
- Rodolphe Jenatton
- Marco F. Duarte
- Simone Scardapane
- Kun Yuan
- Steven L. Brunton
- Christopher A. Metzler
- Namrata Vaswani
Papers from Volkan Cevher's perspective
Ordered by how strongly the researchers Volkan Cevher trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Compressive Sensing for Background Subtraction (2008) — 347 citations
- A Universal Primal-Dual Convex Optimization Framework (2015) — 63 citations
- On the linear convergence of the stochastic gradient method with constant step-size (2017) — 31 citations
- Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics (2014) — 308 citations
- Online Adaptive Methods, Universality and Acceleration (2018) — 110 citations
- Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces (2019) — 859 citations
- DiGress: Discrete Denoising diffusion for graph generation (2022) — 670 citations
- Faster Coordinate Descent via Adaptive Importance Sampling (2017) — 42 citations
- On the linear convergence of the projected stochastic gradient method with constant step-size (2017)
- Combinatorial Penalties: Structure preserved by convex relaxations (2017) — 4 citations
- Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage (2017) — 100 citations
- Stochastic Spectral Descent for Restricted Boltzmann Machines (2015) — 76 citations
- High-Dimensional Gaussian Process Bandits (2013) — 189 citations
- Submodular Dictionary Selection for Sparse Representation (2010) — 138 citations
- Model-Based Compressive Sensing (2008) — 2,612 citations