Neural Architecture Search

Tencent Advertising Technology

Dec 27, 2024 · Artificial Intelligence

Tencent's AutoML Research for Advertising Recommendation Systems

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.

AutoMLEmbedding Size SearchHyperparameter Optimization

0 likes · 10 min read

Tencent's AutoML Research for Advertising Recommendation Systems

IT Architects Alliance

Dec 19, 2024 · Artificial Intelligence

From Traditional IT Architecture Limitations to the Rise of Adaptive Intelligent Architecture

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.

Artificial IntelligenceNeural Architecture Searchadaptive architecture

0 likes · 18 min read

From Traditional IT Architecture Limitations to the Rise of Adaptive Intelligent Architecture

DataFunTalk

Oct 20, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Practices, and Optimizations

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.

AI deploymentAutoMLData Parallelism

0 likes · 22 min read

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Practices, and Optimizations

DataFunTalk

Aug 25, 2023 · Artificial Intelligence

Advances in Graph Neural Architecture Search: GASSO, DHGAS, GAUSS, GRACES, G‑RNA and the AutoGL Library

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.

AutoGLAutoMLGraph Neural Networks

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Advances in Graph Neural Architecture Search: GASSO, DHGAS, GAUSS, GRACES, G‑RNA and the AutoGL Library

DataFunSummit

Feb 22, 2023 · Artificial Intelligence

AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI

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.

Alibaba PAIAutoMLFeature Engineering

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AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI

DataFunTalk

Oct 15, 2022 · Artificial Intelligence

AutoDL: Automated and Interpretable Deep Learning – Research Highlights from Baidu Big Data Lab

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.

AI AutomationAutoDLNeural Architecture Search

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AutoDL: Automated and Interpretable Deep Learning – Research Highlights from Baidu Big Data Lab

DataFunTalk

Sep 7, 2022 · Artificial Intelligence

Pluto: OPPO’s AutoML Tool for Hardware‑Aware Model Compression and Deployment

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.

AutoMLHardware‑AwareNeural Architecture Search

0 likes · 15 min read

Pluto: OPPO’s AutoML Tool for Hardware‑Aware Model Compression and Deployment

DataFunSummit

Aug 14, 2022 · Artificial Intelligence

Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

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.

Neural Architecture Searchknowledge distillationmachine learning

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Optimizing Pre‑Ranking in Meituan Search: Knowledge Distillation and Neural Architecture Search

Tencent Cloud Developer

May 31, 2022 · Artificial Intelligence

Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper

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.

Big DataGraph Neural NetworksNeural Architecture Search

0 likes · 11 min read

Scalable Graph Neural Architecture Search System (PaSca) – WWW 2022 Best Student Paper

DataFunSummit

Jan 20, 2022 · Artificial Intelligence

Green Deep Learning: Sustainable Neural Architecture Search and Vocabulary Optimization

This article introduces Green Deep Learning, explaining its purpose of reducing computational costs while maintaining model performance, and details three core topics: the definition of green AI, training‑free green neural architecture search, and green vocabulary learning techniques for more efficient natural language processing.

Green AINeural Architecture SearchSustainable Computing

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Green Deep Learning: Sustainable Neural Architecture Search and Vocabulary Optimization

DataFunTalk

Dec 24, 2021 · Artificial Intelligence

Large-Scale Pretrained Model Compression and Distillation: AdaBERT, L2A, and Meta‑KD

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.

AINeural Architecture Searchknowledge distillation

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Large-Scale Pretrained Model Compression and Distillation: AdaBERT, L2A, and Meta‑KD

DataFunSummit

Dec 21, 2021 · Artificial Intelligence

Large‑Scale Pretrained Model Compression and Distillation: AdaBERT, L2A, and Meta‑KD

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.

NLPNeural Architecture Searchknowledge distillation

0 likes · 13 min read

Kuaishou Tech

Dec 9, 2021 · Artificial Intelligence

Multi-Task Audio Source Separation (MTASS) and SpeechNAS: AutoML‑Driven Large‑Scale Speaker Recognition

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.

AutoMLMTASSNeural Architecture Search

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Multi-Task Audio Source Separation (MTASS) and SpeechNAS: AutoML‑Driven Large‑Scale Speaker Recognition

Alimama Tech

Nov 10, 2021 · Artificial Intelligence

AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising

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.

AutoMLCVR estimationNeural Architecture Search

0 likes · 15 min read

AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising

DataFunTalk

Apr 30, 2020 · Artificial Intelligence

Weight‑Sharing Neural Architecture Search: Challenges, Methods, and Future Directions

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.

AIAutoMLDARTS

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Weight‑Sharing Neural Architecture Search: Challenges, Methods, and Future Directions

JD Tech Talk

Apr 29, 2020 · Artificial Intelligence

Neural Architecture Search: A Survey – Overview, Methods, and Future 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.

AutoMLNAS SurveyNeural Architecture Search

0 likes · 25 min read

Neural Architecture Search: A Survey – Overview, Methods, and Future Directions

DataFunTalk

Feb 7, 2020 · Artificial Intelligence

Automated Machine Learning for Interaction Functions in Collaborative Filtering

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.

AutoMLInteraction FunctionNeural Architecture Search

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Automated Machine Learning for Interaction Functions in Collaborative Filtering

Top Architect

Jan 16, 2020 · Artificial Intelligence

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

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.

NASNeural Architecture SearchOne-shot

0 likes · 13 min read

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

JD Tech Talk

Dec 5, 2019 · Artificial Intelligence

An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges

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.

AutoMLCase StudyHyperparameter Optimization

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An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges

DataFunTalk

Oct 18, 2019 · Artificial Intelligence

Reinforcement Learning Based Neural Architecture Search: Methods and Advances

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.

AutoMLMonte Carlo Tree SearchNeural Architecture Search

0 likes · 23 min read