mlsanityData to 14 Oct 2026

Learning Trees of ℓ0-Minimization Problems

arXiv 2302.02548 ↗First posted 6 Feb 2023cs.LG cs.NA math.NAcited by 0

Not on the map. Why?

Abstract (arXiv)

The problem of computing minimally sparse solutions of under-determined linear systems is NP hard in general. Subsets with extra properties, may allow efficient algorithms, most notably problems with the restricted isometry property (RIP) can be solved by convex ℓ1-minimization. While these classes have been very successful, they leave out many practical applications. In this paper, we consider adaptable classes that are tractable after training on a curriculum of increasingly difficult samples. The setup is intended as a candidate model for a human mathematician, who may not be able to tackle an arbitrary proof right away, but may be successful in relatively flexible subclasses, or areas of expertise, after training on a suitable curriculum.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

27 of 60 references.

References · 27

  1. 2014How transferable are features in deep neural networks?cited by 1,225
  2. 2017Deep Image Priorcited by 1,098
  3. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  4. 2013DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognitioncited by 424
  5. 2017Compressed Sensing using Generative Modelscited by 339
  6. 2015Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picturecited by 49
  7. 2018Compressed Sensing with Deep Image Prior and Learned Regularizationcited by 47
  8. 2018Neural Proximal Gradient Descent for Compressive Imagingcited by 46
  9. 2019Deep Compressed Sensingcited by 46
  10. 2020Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationcited by 39
  11. 2015Simple, Efficient, and Neural Algorithms for Sparse Codingcited by 37
  12. 2017Global Guarantees for Enforcing Deep Generative Priors by Empirical Riskcited by 37
15 more
  1. 2018Modeling Sparse Deviations for Compressed Sensing using Generative Modelscited by 33
  2. 2019Algorithmic Guarantees for Inverse Imaging with Untrained Network Priorscited by 32
  3. 2015Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Methodcited by 24
  4. 2017Deep Networks for Compressed Image Sensingcited by 23
  5. 2013New Algorithms for Learning Incoherent and Overcomplete Dictionariescited by 22
  6. 2014Dictionary Learning and Tensor Decomposition via the Sum-of-Squares Methodcited by 22
  7. 2018Learning a Compressed Sensing Measurement Matrix via Gradient Unrollingcited by 16
  8. 2018A Provably Convergent Scheme for Compressive Sensing under Random Generative Priorscited by 15
  9. 2014Local Identification of Overcomplete Dictionariescited by 7
  10. 2014More Algorithms for Provable Dictionary Learningcited by 6
  11. 2019Restricted Isometry Property under High Correlationscited by 5
  12. 2018Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurementscited by 4
  13. 2013Sparse Matrix Factorizationcited by 3
  14. 2020A Relaxation Argument for Optimization in Neural Networks and Non-Convex Compressed Sensingcited by 2
  15. 2021Non-Convex Compressed Sensing with Training Datacited by 1

Built on by

Not cited yet.