Each dataset contains miscellaneous series, categorized into six domains (micro, industry, macro, finance, demographic, other). Thus, atime series regression dataset consists ofT input-target pairs: {(X1,y1),...,(XT,yT). For each synthesized training set withT samples, we synthesize100T samples as the testing set. D.3 Models The NN we use has six fully-connected layers with ReLU activation function and three residual connections. D.4 Results There are three methods tobe compared.

We then quantify epistemic discrimination as the gap between a model's accuracy when fairness constraints are

We then quantify epistemic discrimination as the gap between a model's accuracy when fairness constraints are

Neural Information Processing Systems http://nips.cc/

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.

Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small memory buffer; subsequently, they repeatedly optimize on the latter to prevent catastrophic forgetting. This work draws attention to a hidden pitfallofthis widespread practice: repeated optimization onasmall pool of data inevitably leads to tight and unstable decision boundaries, which are a major hindrance to generalization.

Differential equations in general and neural ODEs in particular are an essential technique in continuous-time system identification. While many deterministic learning algorithms have been designed based on numerical integration via the adjoint method, many downstream tasks such as active learning, exploration in reinforcement learning, robust control, or filtering require accurate estimates of predictive uncertainties.