Learning Inference Networks for Structured Prediction Energy Networks
Table 2: Training and test-time inference speed comparison (examples/sec).
['[EMPTY]', 'Training Speed (examples/sec) Bibtex', 'Training Speed (examples/sec) Bookmarks', 'Training Speed (examples/sec) Delicious', 'Testing Speed (examples/sec) Bibtex', 'Testing Speed (examples/sec) Bookmarks', 'Testing Speed (examples/sec) Delicious']
Speed Comparison. We only report speeds of methods that we ran. The SPEN (E2E) times were obtained using code obtained from Belanger and McCallum. We suspect that SPEN (BM16) training would be comparable to or slower than SPEN (E2E). Our method can process examples during training about 10 times as fast as the end-to-end SPEN, and 60-130 times as fast during test-time inference. In fact, at test time, our method is roughly the same speed as the MLP baseline, since our inference networks use the same architecture as the feature networks which form the MLP baseline. Compared to the MLP, the training of our method takes significantly more time overall because of joint training of the energy function and inference network, but fortunately the test-time inference is comparable.