Understanding how and why data distillation methods work is vital not only for improving these methods but also for revealing fundamental characteristics of "good" training data.

The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to "Contrastive Language-Image

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model.

As patients and employers look for alternatives to pricey GLP-1 drugs, Silicon Valley startup Twin Health is using AI and wearable sensors to help people make healthier choices. Rodney Buckley has lost 100 pounds in less than a year, not by using a GLP-1 drug but with the help of a digital twin. Last March, the 55-year-old retired firefighter turned village mayor of Third Lake, Illinois, was 376 pounds. He had tried different diets over the years and would typically lose some weight but eventually gain it back. When his wife's employer started offering a program from startup Twin Health, he thought he would give it a try.

We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).

Representation learning usually involves pretraining a network on a large, diverse dataset using one of a handful of different types of supervision.

PGT -Aug involves three key steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity simulation, and (iii) a hybrid context-aware placement method from ground and map information. We demonstrate the superiority and generality of our method through performance improvements in extensive experiments conducted on popular benchmarks, i.e., nuScenes, KITTI, and Lyft, especially for the datasets with large domain gaps