# Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks

arXiv: https://arxiv.org/abs/1611.00050 · HTML: https://mlsanity.com/paper/1611.00050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

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

- [Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images](https://mlsanity.com/paper/2211.12047/) (2022)
- [Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations](https://mlsanity.com/paper/2101.06848/) (2021)
- [Fast Deep Predictive Coding Networks for Videos Feature Extraction without Labels](https://mlsanity.com/paper/2409.04945/) (2024)

## Abstract (arXiv)

We propose a convolutional recurrent neural network, with Winner-Take-All dropout for high dimensional unsupervised feature learning in multi-dimensional time series. We apply the proposedmethod for object recognition with temporal context in videos and obtain better results than comparable methods in the literature, including the Deep Predictive Coding Networks previously proposed by Chalasani and Principe.Our contributions can be summarized as a scalable reinterpretation of the Deep Predictive Coding Networks trained end-to-end with backpropagation through time, an extension of the previously proposed Winner-Take-All Autoencoders to sequences in time, and a new technique for initializing and regularizing convolutional-recurrent neural networks.