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.

If α > β, we are overemphasizing the contribution of the first term of Eq. 9 (which brings each layer'sλk1 andck close toeach other) overthesecond one(which induces small Lipschitz targets).

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.

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.

Even if the optimal policy is obtained precisely, it is often the case the optimal policy is deterministic.