Abstract: Recurrent neural networks (RNNs) can be trained to process sequences of tokens as they show impressive results in several sequence prediction.
This work proposes a progressive learning strategy that can mitigate the mistakes by using domain knowledge and gently changes the training process from ...
This paper introduces Locally Recurrent Probabilistic Neural Networks (LRPNN) as an extension of the well-known Prob- abilistic Neural Networks (PNN).
In this work, we propose a progressive learning strategy that can mitigate the mistakes by using domain knowledge. Our strategy gently changes the training ...
Progressive Training in Recurrent Neural Networks for Chord Progression Modeling. ... Knowledge Injection to Neural Networks with Progressive Learning Strategy.
Progressive Training in Recurrent Neural Networks for Chord Progression Modeling. V Trung-Kien, R Teeradaj, T Satoshi, N Ha-Thanh, N Le Minh. Proceedings of ...
Research in cognitive neuroscience has identified neural activity correlated with subjects hearing an unexpected event in a musical sequence.
Missing: Progressive | Show results with:Progressive
With the ability to automatically extract the hierarchy of semantic level from the data, neural networks often outperform other techniques in complex issues.
In this paper, we present an audio chord recognition system based on a recurrent neural network. The audio features are obtained from a deep neural network ...
Missing: Progressive | Show results with:Progressive
People also ask
How do you train an RNN model?
What are the issues faced while training in recurrent networks?
What are progressive neural networks?
Why are recurrent neural networks hard to train?
Feb 22, 2020 · We propose an approach of injecting knowledge into the neural network instead of letting it struggles by itself.