Yeah, I should definitely put more detail in the writeup -- thanks for the feedback!
What's happening with figure 1a (epochs vs google accuracy) is that as you train for more epochs the full precision loss continues to decrease (dotted red line) but accuracy also starts decreasing (solid red line). This indicates overfitting (since you'd expect accuracy to increase if loss decreases). The blue lines (quantized training with 1 bit) do not show this which suggests that quantized training seems to act as a form of regularization.
Figure 1b is pretty similar, except on the x axis we have vector dimension. As you increase vector dimension, full precision loss decreases, yet after a certain point full precision accuracy decreases as well. I took this to mean that word2vec training was overfitting with respect to vector dimension.
What's happening with figure 1a (epochs vs google accuracy) is that as you train for more epochs the full precision loss continues to decrease (dotted red line) but accuracy also starts decreasing (solid red line). This indicates overfitting (since you'd expect accuracy to increase if loss decreases). The blue lines (quantized training with 1 bit) do not show this which suggests that quantized training seems to act as a form of regularization.
Figure 1b is pretty similar, except on the x axis we have vector dimension. As you increase vector dimension, full precision loss decreases, yet after a certain point full precision accuracy decreases as well. I took this to mean that word2vec training was overfitting with respect to vector dimension.