Common modifications include:(i)tuning attack parameters (e.g., number ofsteps),(ii)replacing network components to simplify the attack (e.g., removing randomization or non-differentiable components), and(iii) replacing the loss function optimized by the attack.

Each AvehicleC starts sumofcityC, after again.

Inrecentyears,quantitativestatistical and algorithmic treatments of these formulations have produced insights into modern computational methods-resulting innovelapproaches todifficult, open problems.

We find that the inconsistency between training and inference of BN is the leading cause that results in the failure of BN in NLP. We define Training Inference Discrepancy (TID) to quantitatively measure this inconsistencyand reveal that TID can indicate BN'sperformance, supported by extensiveexperiments,includingimageclassification,neuralmachinetranslation, language modeling, sequence labeling, andtextclassification tasks.

We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.

Previous ablation studies showedthatadding WD to normalized nets indeed leads to better generalization [126, 72, 125].

This inspires ustogenerate negativesamples and inject additional implicit biases on the visual features learned by the models.

In Figure 1 (left) weseethat Gaussian posteriors (intheabsenceofdata) optimizedwith Eq. (1) convergetotheirpriorN(w|0,1)[7,68]. In CMV -MF-VI, shown in (b.), prunedunitsconvergetospiked Gaussians.