meta-learning | BestHub

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.

Neural Architecture Searchadaptive architectureartificial intelligence

0 likes · 18 min read

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

DataFunSummit

Jan 23, 2024 · Artificial Intelligence

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

This article presents Tencent TRS's industrial practice of applying meta‑learning and cross‑domain recommendation to address personalization challenges, detailing problem definitions, solution architectures, algorithmic choices such as MAML, deployment strategies, and the cost‑effective outcomes achieved across multiple scenarios.

MAMLcross-domainindustrial AI

0 likes · 16 min read

Meta-Learning and Cross-Domain Recommendation: Industrial Practices at Tencent TRS

DataFunSummit

Dec 3, 2023 · Artificial Intelligence

Shopee Live Personalized CTR Optimization via Calibration‑Based Meta‑Learning

This article presents Shopee's calibration‑based meta‑learning approach for personalized click‑through‑rate prediction in live streaming, detailing business context, modeling goals, model evolution from Calibration4CVR to CBMR, EmbCB and MlpCB optimizations, and multi‑task and multi‑scene extensions that achieve significant AUC and business metric improvements.

Shopeectrmeta-learning

0 likes · 11 min read

Shopee Live Personalized CTR Optimization via Calibration‑Based Meta‑Learning

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

Jul 6, 2023 · Artificial Intelligence

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

This article presents Tencent TRS's industrial deployment of meta‑learning and cross‑domain recommendation, detailing problem definitions, solution architectures, challenges of industrialization, and practical implementations that achieve personalized modeling and cost‑effective multi‑scene recommendation across various online services.

MAMLcross-domainindustrial AI

0 likes · 18 min read

Industrial Practice of Meta‑Learning and Cross‑Domain Recommendation in Tencent TRS

DataFunTalk

Feb 18, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.

AI deploymentAutoMLData Engineering

0 likes · 20 min read

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

AntTech

Jun 22, 2022 · Cloud Computing

Meta Reinforcement Learning Framework for Predictive Autoscaling in Cloud Environments

This article presents a cloud-native, end‑to‑end autoscaling solution that integrates traffic forecasting, CPU utilization meta‑prediction, and a reinforcement‑learning‑based scaling decision module into a fully differentiable system, achieving higher resource utilization and cost efficiency as demonstrated by ACM SIGKDD 2022 research.

autoscalingcapacity-managementcloud computing

0 likes · 10 min read

Meta Reinforcement Learning Framework for Predictive Autoscaling in Cloud Environments

Alimama Tech

Feb 23, 2022 · Artificial Intelligence

Meta‑Network Based Multi‑Scenario Multi‑Task Model (M2M) for Alibaba Advertising Merchants

The paper introduces a Meta‑Network based Multi‑Scenario Multi‑Task (M2M) model for Alibaba’s advertising merchants, combining a transformer‑driven backbone with scene‑aware meta‑learning modules to jointly predict spend, clicks and activity across diverse ad scenarios, achieving up to 27 % error reduction offline and over 2 % lifts in merchant activity and ARPU online.

Alibabaadvertisinge-commerce

0 likes · 14 min read

Meta‑Network Based Multi‑Scenario Multi‑Task Model (M2M) for Alibaba Advertising Merchants

DataFunSummit

Nov 26, 2021 · Artificial Intelligence

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine

This talk by a Stanford PhD student explores how graph neural networks can be adapted for molecular and biomedical networks, discusses the limitations of standard GNNs, introduces novel methods such as SkipGNN and G‑Meta, and demonstrates their use for drug‑drug interaction prediction, hypothesis generation, and treatment discovery with few‑shot learning.

Biomedical ApplicationsGraph Neural NetworksMolecular Networks

0 likes · 9 min read

Graph Machine Learning for Molecular Networks: Challenges, Methods, and Applications in Biomedicine

Alimama Tech

Oct 20, 2021 · Artificial Intelligence

Highlights of Recent Alibaba Advertising Research Papers Presented at WSDM 2022

At WSDM 2022, Alibaba’s advertising team presented four papers introducing a meta‑learning multi‑task multi‑scenario model for advertiser forecasting, a low‑cost Feature Co‑Action Network that boosts CTR prediction, an Adaptive Unified Allocation Framework that improves guaranteed display fulfillment and CTR, and a cooperative‑competitive multi‑agent auto‑bidding system that enhances both advertiser welfare and platform profit.

CTR predictionadvertisingmachine learning

0 likes · 11 min read

Highlights of Recent Alibaba Advertising Research Papers Presented at WSDM 2022

DataFunSummit

Aug 21, 2021 · Artificial Intelligence

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

This article reviews algorithmic solutions for cold‑start recommendation, covering the efficient use of side information, knowledge graphs, cross‑domain transfer, multi‑behavior signals, limited interaction data, explore‑exploit tactics, and additional practical scenarios, while summarizing key methods such as DropoutNet, MetaEmbedding, MWUF, MeLU and MetaHIN.

Cold StartKnowledge Graphcross-domain

0 likes · 11 min read

Cold‑Start Recommendation: Algorithmic Approaches and Strategies

AntTech

Mar 21, 2021 · Artificial Intelligence

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

The article describes the Hubble Intelligent Audience Platform’s three‑generation algorithmic evolution—starting from a DSSM‑based model, moving to an asynchronous GNN plus lightweight learning architecture, and finally integrating incremental learning with meta‑weighting—to improve audience expansion for mobile marketing campaigns.

AIMobile Marketingaudience expansion

0 likes · 14 min read

Hubble Intelligent Audience Platform: Three‑Generation Algorithm Evolution for Mobile Marketing

DataFunSummit

Mar 16, 2021 · Artificial Intelligence

Myths and Misconceptions in Reinforcement Learning – Summary of Csaba Szepesvári’s KDD 2020 Deep Learning Day Talk

This article summarizes Csaba Szepesvári’s 2020 KDD Deep Learning Day presentation on common myths and misconceptions in reinforcement learning, covering the scope of RL, safety concerns, generalization challenges, causal reasoning, and broader meta‑considerations for the field.

GeneralizationMisconceptionsMyths

0 likes · 16 min read

Myths and Misconceptions in Reinforcement Learning – Summary of Csaba Szepesvári’s KDD 2020 Deep Learning Day Talk

Sohu Tech Products

Feb 24, 2021 · Artificial Intelligence

EdgeRec: Edge Computing in Recommendation Systems

EdgeRec explores how moving recommendation system components to the edge—leveraging real‑time user behavior, heterogeneous action modeling, on‑device reranking, mixed‑ranking, and personalized “thousand‑person‑one‑model” training—can reduce latency, improve relevance, and boost business metrics compared to traditional cloud‑centric pipelines.

Edge Computingmeta-learningmobile AI

0 likes · 19 min read

EdgeRec: Edge Computing in Recommendation Systems

DataFunTalk

Feb 17, 2021 · Artificial Intelligence

EdgeRec: Leveraging Edge Computing for Real‑Time Recommendation Systems

This article presents EdgeRec, a comprehensive edge‑computing framework for recommendation systems that redesigns the architecture, introduces on‑device real‑time user perception, proposes a context‑aware reranking model (CRBAN), details on‑device mixing and training pipelines, and demonstrates significant business improvements through extensive experiments and deployments.

Edge Computingmeta-learningon-device reranking

0 likes · 19 min read

EdgeRec: Leveraging Edge Computing for Real‑Time Recommendation Systems

DataFunTalk

Dec 30, 2020 · Artificial Intelligence

Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI

This article presents a meta‑learning based end‑to‑end task‑oriented dialogue system that quickly adapts to new scenarios with limited data and improves robustness through a human‑machine collaboration decision module, validated on extended‑bAbI benchmarks and real‑world Alibaba Cloud customer‑service applications.

Human-Machine CollaborationMAMLdialogue system

0 likes · 15 min read

Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI

JD Tech Talk

Dec 29, 2020 · Artificial Intelligence

Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning

The paper proposes a deep meta‑learning framework that generates spatio‑temporal representations for retail sales forecasting, especially during large shopping festivals, by combining amortization networks, shared statistical structures, and alternating spatial‑temporal training to achieve robust and accurate predictions despite scarce historical data.

Sales Forecastingdeep learningmeta-learning

0 likes · 9 min read

Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning

DataFunTalk

Dec 23, 2020 · Artificial Intelligence

Advances in Knowledge Graph Completion: Methods, Challenges, and Future Directions

This article reviews the rapid progress of knowledge graph completion, covering its background, formal problem definition, major technical approaches—including representation learning, path‑based search, reinforcement learning, logical reasoning, and meta‑learning—while discussing their challenges, recent improvements, and promising future research directions.

Knowledge Graphcompletionlogical reasoning

0 likes · 14 min read

Advances in Knowledge Graph Completion: Methods, Challenges, and Future Directions

Tencent Advertising Technology

Sep 22, 2020 · Artificial Intelligence

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

This article summarizes the Q&A session of the 2020 Tencent Advertising Algorithm Competition live series, covering the fundamentals of automated machine learning, its key technologies, current challenges, and the features and advantages of the SolnML system, while also addressing practical concerns such as hardware support and future research directions.

AIAutoMLHyperparameter Optimization

0 likes · 13 min read

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

DataFunTalk

Nov 22, 2019 · Artificial Intelligence

Machine Reasoning for Multi‑turn Semantic Parsing and Question Answering

This article reviews recent advances in machine reasoning applied to multi‑turn semantic parsing and conversational question answering, describing how grammar, context, and data knowledge are integrated via sequence‑to‑action models and meta‑learning to achieve state‑of‑the‑art results on the CSQA benchmark.

conversational QAmachine reasoningmeta-learning

0 likes · 8 min read

Machine Reasoning for Multi‑turn Semantic Parsing and Question Answering