Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

This motivates us to consider a challenging classification problem with intrinsic cluster structures.

Most existing point cloud detection models require large-scale, densely annotated datasets. They typically underperform in domain adaptation settings, due to geometry shifts caused by different physical environments or LiDAR sensor configurations. Therefore, itischallenging butvaluable tolearn transferable features between a labeled source domain and a novel target domain, without any access to target labels. To tackle this problem, we introduce the framework of 3DContrastiveCo-training (3D-CoCo) with two technical contributions. First, 3D-CoCo is inspired by our observation that the bird-eye-view (BEV) features are more transferable than low-levelgeometry features.

This problem is important to robustly learning from data that are corrupted by,e.g., random noise and adversarial examples. The standard empirical risk minimization (ERM) for such data, however, may easily overfit noise and thus suffers from sub-optimal performance. In this paper, we observe that model predictions can substantially benefit the training process: self-adaptive training significantly mitigates the overfitting issue and improves generalization over ERM under both random and adversarial noise.

The giant formula for simultaneously enlarging the resolution, depth and width provides us aRubik'scube for neural networks. So that we can find networks with high efficiency and excellent performance by twisting the three dimensions.