DNNs, such as adversarial training (Madry et al., 2018), provide no provable robustness guarantees, Both IBP and CROWN-IBP with loss fusion (Xu et al., 2020) have a per-batch training time For example, generalized CROWN-IBP in Xu et al. (2020) used 900 epochs for warmup and 2,000 He et al., 2015a), but prior works for certified training generally use weight initialization methods originally designed for standard DNN training, while certified training is essentially optimizing a different type of augmented network defined by robustness verification (Zhang et al., 2020). It can however hamper classification performance if too many neurons are dead.

A few past works approach this problem by adopting a self-training framework with a sample-selection mechanism.

Nonetheless, uncertainty estimation for non-independent node-level predictions is under-explored.

However, the simple power-law relation becomes more complicated when compute is considered.

Additionally, to address the inefficiency of matrix transformations due to the vast discrete space, we use semantic labels derived from quantization or RQ-V AE to replace item IDs, enhancing efficiency and improving cold start issues.

End-to-end object detection with transformers.

The former task requires the model to learn to reason over a given image following program guidance.