# Convolutional Bipartite Attractor Networks

arXiv: https://arxiv.org/abs/1906.03504 · HTML: https://mlsanity.com/paper/1906.03504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Hierarchical Associative Memory](https://mlsanity.com/paper/2107.06446/) (2021)
- [Capturing the objects of vision with neural networks](https://mlsanity.com/paper/2109.03351/) (2021)
- [Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss](https://mlsanity.com/paper/2102.09808/) (2021)

## Abstract (arXiv)

In human perception and cognition, a fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambiguous evidence. The problem of interpretation is well matched to an early and often overlooked architecture, the attractor network---a recurrent neural net that performs constraint satisfaction, imputation of missing features, and clean up of noisy data via energy minimization dynamics. We revisit attractor nets in light of modern deep learning methods and propose a convolutional bipartite architecture with a novel training loss, activation function, and connectivity constraints. We tackle larger problems than have been previously explored with attractor nets and demonstrate their potential for image completion and super-resolution. We argue that this architecture is better motivated than ever-deeper feedforward models and is a viable alternative to more costly sampling-based generative methods on a range of supervised and unsupervised tasks.