From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
In recent studies [1] [2] [3] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is also based on prediction. What the problem comes down to is whether a data compre...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/65946 http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/ASAI/asai-10.pdf |
| Aporte de: |
| Sumario: | In recent studies [1] [2] [3] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey we discovered what we think is the fundamental difference between a Data Compression Algorithm and a Recurrent Neural Network. |
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