# Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps

arXiv: https://arxiv.org/abs/1805.07798 · HTML: https://mlsanity.com/paper/1805.07798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2018.

## Built on by (most cited first, 6 of 6)

- [From Local Pseudorandom Generators to Hardness of Learning](https://mlsanity.com/paper/2101.08303/) (2021)
- [Exponentially Many Local Minima in Quantum Neural Networks](https://mlsanity.com/paper/2110.02479/) (2021)
- [On the hardness of learning under symmetries](https://mlsanity.com/paper/2401.01869/) (2024)
- [What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement](https://mlsanity.com/paper/2303.11249/) (2023)
- [Module-wise Training of Neural Networks via the Minimizing Movement Scheme](https://mlsanity.com/paper/2309.17357/) (2023)
- [A New Measure of Model Redundancy for Compressed Convolutional Neural Networks](https://mlsanity.com/paper/2112.04857/) (2021)

## Abstract (arXiv)

We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models.