Since DARTS' code is published, a fair comparison is attainable: In Bold are the results of new experiments following the reviewer's request. For clarity, we will add'XNAS-plain' FLOPs and inference time comparisons were out of this paper's scope, and We will report our results in the revised experiments section. EG/Wipeout ablation: Please find EG/Wipeout ablation below. Paper clarity: the revised version now includes clearer explanations and modified figures as remarked by the reviewer. For citation we simply used the command cite with NeurIPS style.

--Strong priors are imposed on the search space of Differentiable Architecture Search (DARTS), such that cells of the same type share the same topological structure and each intermediate node retains two operators from distinct nodes. While these priors reduce optimization difficulties and improve the applicability of searched architectures, they hinder the subsequent development of automated machine learning (Auto-ML) and prevent the optimization algorithm from exploring more powerful neural networks through improved architectural flexibility. This paper aims to reduce these prior constraints by eliminating restrictions on cell topology and modifying the dis-cretization mechanism for super-networks. Specifically, the Flexible DARTS (FX-DARTS) method, which leverages an Entropy-based Super-Network Shrinking (ESS) framework, is presented to address the challenges arising from the elimination of prior constraints. Notably, FX-DARTS enables the derivation of neural architectures without strict prior rules while maintaining the stability in the enlarged search space. Experimental results on image classification benchmarks demonstrate that FX-DARTS is capable of exploring a set of neural architectures with competitive trade-offs between performance and computational complexity within a single search procedure. Derong Liu is with the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518000, China (email: liudr@sustech.edu.cn), and also with the Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607, USA (e-mail: derong@uic.edu). This manuscript is submitted to IEEE Transaction on Neural Network and Learning Systems and is under reviewed. Personal use of this manuscript is permitted. Over the past decade, the powerful representation ability of deep neural networks (DNNs) has contributed to significant progress in various machine learning tasks, including computer vision [1], [2], natural language processing [3], [4], system identification and control [5], [6], time series prediction [7], and autonomous vehicles [8]-[10], among others. Undoubtedly, the architecture design of neural networks plays a pivotal role in these breakthroughs [11]-[15]. To address the labor-intensive and time-consuming trial-and-error process of DNN architecture design, neural architecture search (NAS) [16] has emerged as a promising approach. NAS automates the exploration of a vast space of potential architectures, traditionally through three key steps: defining a search space, selecting a search algorithm, and identifying an optimal architecture within the search space. The effectiveness of NAS heavily relies on the careful design of both the search space and the search strategy, as a well-constructed search space can significantly enhance the search algorithm's ability to discover optimal neural architectures [17].

Prior work found that superhuman Go AIs like KataGo can be defeated by simple adversarial strategies. In this paper, we study if simple defenses can improve KataGo's worst-case performance. We test three natural defenses: adversarial training on hand-constructed positions, iterated adversarial training, and changing the network architecture. We find that some of these defenses are able to protect against previously discovered attacks. Unfortunately, we also find that none of these defenses are able to withstand adaptive attacks. In particular, we are able to train new adversaries that reliably defeat our defended agents by causing them to blunder in ways humans would not. Our results suggest that building robust AI systems is challenging even in narrow domains such as Go. For interactive examples of attacks and a link to our codebase, see https://goattack.far.ai/.

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs. The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available https://goattack.far.ai/.

Neural architecture search is the task of automatically finding one or more architectures for a neural network that will yield models with good results (low losses), relatively quickly, for a given dataset. Neural architecture search is currently an emergent area. There is a lot of research going on, there are many different approaches to the task, and there isn't a single best method generally -- or even a single best method for a specialized kind of problem such as object identification in images. Neural architecture search is an aspect of AutoML, along with feature engineering, transfer learning, and hyperparameter optimization. It's probably the hardest machine learning problem currently under active research; even the evaluation of neural architecture search methods is hard.