Pruning methods for neural networks [LeCun et al., 1989] replace dense weight matrices by sparse

In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.

We study the problem of online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems.

We conduct extensive experiments on various benchmark datasets, empirically demonstrate the effectiveness of Terra.

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values.

For the rest 10k iterations, we further finetune the shared volume and the RGB branch.

Given the super-rich semantics in 3D scenes, a crucial aspect of this task is achieving open-vocabulary segmentation that can handle regions and objects of various semantics including those with long-tail distributions.

Manyfew-shot segmentation (FSS) methods use cross attention to fuse support foreground (FG) into query features, regardless of the quadratic complexity.