Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm; the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.
DASViT: Differentiable Architecture Search for Vision Transformer
School of Computer Science and Electronic Engineering University of Surrey（计算机科学与电子工程学院大学 Surrey 大学）；School of Artificial Intelligence and Computer Science Jiangnan University（人工智能与计算机科学学院江南大学）
Evolutionary Negative Module Pruning for Better LoRA Merging
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；State Key Laboratory of Blockchain and Security, Zhejiang University（浙江大学区块链与安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization
School of Computer and Communication Engineering, Northeastern University（东北大学计算机与通信工程学院）；School of Software Engineering, Beijing Jiaotong University（北京交通大学软件工程学院）；School of Software, Northeastern University（东北大学软件学院）
NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation
Hong Kong Baptist University（香港浸会大学）；Hong Kong University of Science and Technology（香港科技大学）；Microsoft Research Asia（微软亚洲研究院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Georgia Institute of Technology（佐治亚理工学院）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Massachusetts Institute of Technology（麻省理工学院）；University of Wuppertal（伍珀塔尔大学）；Deep Principle Inc.（Deep Principle公司）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Cornell University（康奈尔大学）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）
