SlowGuess/NeurIPS_2023_OCR huggingface dataset:
MLP achieved higher accuracy for emulating the forward model. However, this higher performance did not translate to the inference stage. MLP was able to perform well when given a more accurate starting point (GPR init), but failed to outperform GPR with the average initialization, getting caught in local minimas. PETAL was able to outperform GPR regardless of initialization. This suggests that the structure embedded into PETAL's architecture aids in the optimization process when determining descent directions. Examples of reconstructed SSPs can be found in Figure 8. Note the artifacts and hallucinations introduced by optimizing the MLP surrogate.
Our proposed model was evaluated on noise-less simulations, both with respect to measurements and sensor/receiver placement, which is not true in practice for data collected in the real world. We also did not explore the selection process of the reference points to linearize around, assuming that the
Table 6: RMSE (m/s) of random data split
Model	Optimization	Avg Init	GPR Init
GPR	—	0.176	—
PETAL	SGD	0.144	0.128
PETAL	LBFGS	0.142	0.129
MLP	SGD	0.224	0.146
MLP	LBFGS	0.231	0.164
Figure 8: Example visualizations of predicted sound speed profiles for each method. The "S-" prefix denotes models optimized with SGD and "L-" with LBFGS.
chosen subset sufficiently represented the data. However, section B.2 suggests that the selection of reference points is somewhat robust to unseen dynamics.