RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Figure 11: Curvature convergence behavior also holds for Adam optimizer . As explained in Methods, we used SGD optimizer due to confounding factors in Adam optimizer that could convolute our matching step length analysis in Figure 4 . We observe similar trends as in Figure 3 . Plotting conventions follow that of previous figures.; Curvature convergence behavior also holds for Adam optimizer . As explained in Methods, we used SGD optimizer due to confounding factors in Adam optimizer that could convolute our matching step length analysis in Figure 4 . We observe similar trends as in Figure 3 . Plotting conventions follow that of previous figures.
Figure 12: We repeat the generalization gap vs leading Hessian eigenvalue scatter plot in Figure 2 with the learning rule fixed for A) BPTT and B) the three-factor rule. As expected, a significant correlation between the generalization gap and leading Hessian eigenvalue is observed. Unlike Figure 2, where the hyperparameters were fixed for each rule (tuned using the procedure in Appendix A.3 ), the learning rate is varied here in order to get a wide enough curvature range to observe the correlation. C) The matching step experiments in Figure 4 were repeated here with the learning rate increased by three times for all rules, and the observation agrees with that in Figure 4 . Plotting convention follows that of previous figures.; We repeat the generalization gap vs leading Hessian eigenvalue scatter plot in Figure 2 with the learning rule fixed for A) BPTT and B) the three-factor rule. As expected, a significant correlation between the generalization gap and leading Hessian eigenvalue is observed. Unlike Figure 2, where the hyperparameters were fixed for each rule (tuned using the procedure in Appendix A.3 ), the learning rate is varied here in order to get a wide enough curvature range to observe the correlation. C) The matching step experiments in Figure 4 were repeated here with the learning rate increased by three times for all rules, and the observation agrees with that in Figure 4 . Plotting convention follows that of previous figures.
BPTT stopped early to match the test accuracy of the three-factor rule for the sequential MNIST task. Higher generalization gap and leading Hessian eigenvalue is again observed for the three-factor rule, as expected. Each rule is repeated for five runs with different random weight initialization.