Moreover, wehighlight unitsphere, theequivalence (31) holds. D.1 Equi Thecontrasti distribution,y = 0 is attractiveP L (f (xe),f (xa)) = 1 2 kf (xe) f (xa)k2 = logc ) logP (f (xe)|xa, ) whileaney= <0isrelated P L (f (xe),f (xa)) = 1 2 kf (xe) f (xa)k2 = 2 + 1 2 kf (xe)+ f (xa)k2 = 2 + logc ) logP (f (xe)|xa, ).

DPMs, those inherent factors can be automatically discovered, explicitly represented, and clearly injected into the diffusion process via the sub-gradient fields. To tackle this task, we devise an unsupervised approach named DisDiff, achieving disentangled representation learning in the framework of DPMs.

We introduce a multi-task setting for PCoTT A, which is practical and realistic, handling multiple tasks within one unified model during the continual adaptation.

The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a "simplicity bias": neural networks prevent overfitting by initially learning simple classifiers before progressing to

With coocmap, 10-40MB of text data and a few minutes of CPU time is sufficient to achieve unsupervised word translation if the training corpora are in the same domain (e.g. both on