掌握AI：大数据、深度学习与大型语言模型的演变——从基础到最新技术的AutoML

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches …

AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting

Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traffic Forecasting (TF). TF is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. However, TF poses one big challenge to the ML paradigm, known as the Model Selection Problem (MSP): deciding the most suitable combination of data preprocessing techniques and ML method for traffic data collected under different transportation circumstances. In this context, Automated …

AutoML to Date and Beyond: Challenges and Opportunities

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML's main selling points, the process still requires human involvement at a number of vital steps, includin…

自动化机器学习：商业分析中的人工智能驱动决策

Automated machine learning: AI-driven decision making in business analytics

本文分析了AutoML在商业分析中的应用潜力，通过将H2O AutoML框架与手动调优的堆叠ML模型在三个真实数据集上对比，发现AutoML虽略逊一筹但快速易用且结果可靠，能有效缩短开发周期并弥合专家供需差距。

面向深度学习与大数据应用的自动化数据处理与特征工程：综述

Automated data processing and feature engineering for deep learning and big data applications: a survey

该综述全面梳理了深度学习与大数据应用中自动化数据处理与特征工程的方法，涵盖数据预处理、增强及特征工程，并探讨了AutoML在端到端流水线优化中的作用。

高通量棉花表型大数据流水线：Lambda架构、计算机视觉与深度神经网络

High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks

本研究提出一种基于Lambda架构和Azure云服务的棉花开花检测大数据流水线，利用YOLOv5模型实现高精度（mAP 0.96）和高效处理（9000张图像34分钟），为精准农业提供了可扩展的深度学习解决方案。