The open‑source ASI‑EVOLVE framework lets AI autonomously design AI across model architecture, data curation, and reinforcement‑learning algorithms, achieving up to three times the human‑level state‑of‑the‑art performance and demonstrating cross‑domain gains in drug‑target prediction.
This article reviews Neural Architecture Search (NAS), explains its bi‑level optimization formulation, compares three major search strategies—reinforcement learning, evolutionary algorithms, and differentiable gradient‑based methods—provides complete Python code for each, and analyzes experimental results highlighting performance trade‑offs and remaining challenges.
This article outlines Tencent's AutoML research, presenting several recent papers that introduce novel neural architecture search, feature selection, pooling, embedding size, and hyper‑parameter optimization techniques to improve the efficiency, accuracy, and scalability of large‑scale advertising recommendation systems.
Traditional IT architectures suffer from manual, passive operations and limited scalability, prompting a shift toward adaptive intelligent architectures that leverage neural architecture search, elastic networks, and meta‑learning to dynamically adjust models across domains such as autonomous driving, mobile devices, robotics, and personalized recommendation, while addressing efficiency, generalization, and real‑time challenges.
This article describes how Du Xiaoman tackled the high cost, instability, and long cycles of AI algorithm deployment by building the ATLAS automated machine learning platform, detailing its four‑stage workflow, component platforms, scaling and efficiency techniques, and practical Q&A for practitioners.
This article surveys recent progress in automated graph machine learning, covering graph neural architecture search techniques such as GASSO, DHGAS, GAUSS, GRACES, and G‑RNA, discusses scalability and robustness challenges, and introduces the open‑source AutoGL library and the NAS‑Bench‑Graph benchmark.
This article introduces AutoML, explaining how it automates data cleaning, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and showcases Alibaba PAI's implementations of HPO, AutoFE, and NAS with practical case studies and performance results.
This article reviews Baidu Big Data Lab's recent advances in automated deep learning (AutoDL), covering its research breakthroughs, integration with PaddlePaddle/PaddleHub, industrial deployments, transfer learning innovations, and future directions for AI automation and interpretability.
This article introduces OPPO’s self‑developed AutoML platform Pluto, explains why automated machine learning and model compression are essential for industrial AI, describes Pluto’s hardware‑aware and uniform algorithm framework, showcases typical applications such as video super‑resolution, and provides a detailed Q&A on its methodology and performance.
This article describes Meituan Search's pre‑ranking (coarse‑ranking) system evolution and presents two major optimization strategies—leveraging knowledge distillation to align coarse‑ranking with fine‑ranking and employing neural architecture search to jointly improve effectiveness and latency—demonstrating significant offline and online performance gains.
Meituan’s search team upgraded its pre‑ranking layer from simple linear models to end‑to‑end neural networks, boosting effectiveness by applying three knowledge‑distillation techniques—including result‑list, score, and contrastive representation transfer—and by using latency‑aware neural architecture search to automatically select features and network structures, achieving significant recall and CTR gains without added latency.
PaSca, a scalable graph neural architecture search system that separates message aggregation from updates, explores over 150,000 GNN designs with multi‑objective optimization, delivers models that outperform traditional GNNs in accuracy, memory and speed, has been open‑sourced and deployed at Tencent for risk control, recommendation and fraud detection, and earned the WWW 2022 Best Student Paper award.
This article reviews three consecutive works from Alibaba DAMO Academy on compressing and distilling large pretrained language models—AdaBERT, L2A, and Meta‑KD—detailing their motivations, neural‑architecture‑search‑based designs, loss formulations, experimental results, and insights from a Q&A session.
This talk presents Alibaba DAMO Academy’s recent work on compressing large pretrained language models, covering task‑adaptive AdaBERT, data‑augmented L2A, and meta‑knowledge distillation Meta‑KD, describing their motivations, architectures, NAS‑based search, loss designs, and experimental results across multiple NLP tasks.
This article presents two ASRU‑2021 accepted works from Kuaishou: MTASS, a multi‑task audio source separation framework that jointly separates speech, music and noise, and SpeechNAS, an AutoML‑based neural architecture search method that achieves state‑of‑the‑art speaker recognition performance with significantly fewer parameters.
AutoHERI, a hierarchical representation automatic aggregation model discovered via one‑shot neural architecture search, jointly learns CTR and CVR (and other downstream tasks) to capture cascade relationships, achieving superior AUC and conversion‑rate lifts in large‑scale Alibaba advertising datasets and prompting full production deployment.
The paper introduces DARTS‑, a robust differentiable architecture search method that adds a linearly decaying auxiliary skip‑connection weight to prevent performance collapse, delivering smoother loss landscapes, lower Hessian spikes, and state‑of‑the‑art accuracy on CIFAR‑10, ImageNet and NAS‑Bench‑201, while maintaining efficiency for large‑scale AutoML deployments.
This article reviews the major challenges of AI—data, model, and knowledge—explains why automated machine learning and neural architecture search are crucial, analyzes weight‑sharing NAS algorithms and their instability, presents various improved DARTS‑based methods, and discusses experimental results and future research directions.
This article surveys the field of Neural Architecture Search (NAS), reviewing its motivations, categorizing methods by search space, search strategy, and performance evaluation, summarizing historical approaches, recent advances, and outlining promising future research directions.
This article presents a comprehensive study on using automated machine learning (AutoML) to design interaction functions for collaborative filtering, introducing the SIF framework, detailing its search space, one‑shot algorithm, neural architecture search integration, and demonstrating superior performance on benchmark recommendation datasets.
This article surveys neural architecture search, classifying existing methods, describing common search spaces—including global and cell‑based designs—detailing optimization strategies such as reinforcement learning, evolutionary algorithms, surrogate models, one‑shot and differentiable approaches, and highlighting recent results and trends in the field.
This article provides a comprehensive overview of Automated Machine Learning (AutoML), covering its definition, objectives, research areas, hyperparameter optimization methods, pipeline construction, major CASH algorithms, open-source frameworks such as AutoSklearn and NNI, practical case studies, and current open research challenges.
This article reviews reinforcement‑learning‑driven neural architecture search, covering layer‑based, block‑based, and connection‑based strategies, as well as advanced techniques such as inverse reinforcement learning, graph hyper‑networks, Monte‑Carlo tree search, and knowledge‑distillation‑based model compression.
This article provides a comprehensive overview of AutoML, describing its motivation, formal definition, typical machine‑learning pipeline, key challenges, various optimizer and evaluator strategies—including simple search, heuristic, model‑based, reinforcement learning, and meta‑learning approaches—along with practical applications and future prospects.
AutoML and Neural Architecture Search automate deep‑learning model design by using controller networks to explore and evaluate candidate architectures, with efficient variants like PNAS and ENAS reducing cost, while platforms such as Google Cloud AutoML and open‑source AutoKeras make these techniques accessible, promising broader, democratized AI breakthroughs.
AutoML and Neural Architecture Search automate deep‑learning model design by sampling and training network blocks, using reinforcement‑learning or efficient weight‑sharing strategies such as PNAS and ENAS, enabling high‑accuracy architectures in days on a single GPU, with services like Google Cloud AutoML and open‑source tools like AutoKeras, while future research aims to expand search spaces beyond hand‑crafted blocks.
