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Found 4 papers, 1 papers with code
Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex
The model is a hierarchical recurrent neural model that learns to predict video sequences using the incoming video signals as teaching signals.
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets.
A Model Cortical Network for Spatiotemporal Sequence Learning and Prediction
In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet) to understand how spatiotemporal memories might be learned and encoded in a representational hierarchy for predicting future video frames.
A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction
Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit, instantiated in a LSTM module, to generate a prediction or explanation of the incoming signals.
