Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
Table 6: Runtime of the LFLDA and the N-VAE on the N20short dataset with various numbers of topics.
['Dataset', 'Number of topics', 'LFLDA (s)', 'N-VAE (s)']
We compare the runtime between the LFLDA and the N-VAE. Even though a GPU could be utilized to speed up the training process of our model, we only use the CPU for fairness. Overall, our model requires less than 1/30th the training time of the LFLDA to reach the desired performance. This is the largest advantage of our model, enabling training on a large corpus while requiring only a small amount of time.