The method scales well with the number of points, and it can be used to find the global solution for large-scale problems with thousands of points.

Scaling of Transformers (V aswani et al., 2017) has produced language models (LM) with impressive

Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems. While neural approaches capable of high-quality solutions in a single shot are emerging, state-of-the-art approaches are often unable to take full advantage of the solving time available to them. In contrast, hand-crafted heuristics perform highly effective search well and exploit the computation time given to them, but contain heuristics that are difficult to adapt to a dataset being solved.

In order to accelerate the NES search phase, we generated the pool using the weight sharing schemes proposed by Random Search with WeightSharing[37]andDARTS[39]. Specifically, we trained one-shot weight-sharing models usingeachof these two algorithms, then we sampled architectures from the weightshared models uniformly at random to build the pool.

In this work, we study a lightweight defense method,dubbed RandomNoiseDefense (RND),whichaddsproperGaussiannoise toeachquery.