The discovery of dynamical systems models plays a fundamental role across various domains, including pharmacology, epidemiology, and physical systems.

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

Predicting 3D geometry in dynamic scenes is a challenging problem. In this problem setup, we are given access to multiple images of a scene taken sequentially, e.g., from a monocular video

In the realm of artificial intelligence, continual learning has become an area of significant interest.

A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D ( e.g., class name and bounding box) and 3D information ( e.g., 3D location and 3D viewpoint) for arbitrary rigid objects in natural