GANs trained by a two time-scale update rule converge to a local Nash equilibrium.

This procedure provides a solution to the fundamental kernel selection problem as we can aggregate a large number of kernels with several bandwidths without incurring a significant loss of test power.

In Appendix H, we analyze the statistical stability of reported robust accuracy for TRS ensemble against attacks with random start, and TRS ensemble claims its stability by showing small standard deviation.

The performance of the proposed algorithm is evaluated on a wide variety of machine learning tasks.

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In this regard, the design of stronger adversarial attacks plays a crucial role in understanding the nature of possible real-world threats.

To better understand this trade-off, we observe that sparse and low-rank approximations excel in different regimes, determined by the softmax temperature in attention, and sparse + low-rank can outperform each individually.

Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target. However, UDA is not always successful and several accounts of'negative transfer' have been reported in the