End-to-end Network for Twitter Geolocation Prediction and Hashing
Table 7: Classification performance for deepgeo with the addition of noise and loss term l.
['[ITALIC] R', '[BOLD] deepgeo', '[BOLD] deepgeo + [BOLD] noise', '[BOLD] deepgeo + [BOLD] loss']
We see that the addition of noise and l helps in pushing the elements to the extrema. The noise term appears to work a little better than l, as the frequency for the −1.0 and +1.0 bins is higher. We also observe that there is a small increase in middle/zero values as R increases from 100 to 400, suggesting that there are more unused hidden units when number of parameters increases. The performance drops a little, but generally stays within a gap of 1%. This suggests that both noise and l works well in binarising r without trading off classification accuracy significantly.