active learning

Tencent Advertising Technology

Nov 8, 2024 · Artificial Intelligence

Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction

This article explores advanced machine learning techniques for optimizing bid shading in real-time advertising auctions, introducing a mixed censorship multi-task learning framework and a cost-effective active learning strategy to accurately predict winning price distributions and overcome sample selection bias.

Auction MechanismsBid ShadingReal-Time Bidding

0 likes · 16 min read

Optimizing Real-Time Bidding: Machine Learning Approaches for Bid Shading and Winning Price Prediction

DataFunTalk

Jun 20, 2024 · Artificial Intelligence

User Profiling Algorithms: From Ontology‑Based Methods to Deep Learning and Large Model Integration

This article provides a comprehensive overview of user profiling algorithms, covering the evolution from ontology‑based traditional methods to modern deep‑learning approaches, including structured label prediction, representation learning, active learning, and large‑model integration, while discussing challenges, practical applications, and future research directions.

Large Modelsactive learningdeep learning

0 likes · 26 min read

User Profiling Algorithms: From Ontology‑Based Methods to Deep Learning and Large Model Integration

DataFunTalk

Apr 2, 2024 · Artificial Intelligence

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

This article provides a comprehensive overview of user portrait algorithms, covering their historical development, ontology‑based traditional approaches, deep‑learning enhancements, representation‑learning techniques such as lookalike, active‑learning driven iteration, and the integration of large‑model world knowledge, while also discussing current challenges and future research directions.

Recommendation systemsactive learningdeep learning

0 likes · 26 min read

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

Model Perspective

Mar 16, 2024 · Artificial Intelligence

What Watching a TV Drama Reveals About AI Model Training and Learning Strategies

The article draws parallels between expert viewers dissecting the drama "The Legend of Zhen Huan," efficient paper‑reading techniques, and the active‑prediction plus contrast‑learning approach that underpins modern AI model training, highlighting how proactive thinking boosts both personal and machine learning outcomes.

AI trainingactive learningcontrast learning

0 likes · 8 min read

What Watching a TV Drama Reveals About AI Model Training and Learning Strategies

DataFunSummit

Nov 17, 2023 · Artificial Intelligence

Semantic‑Aware Active Learning on Graph Data for Risk Control: Tackling Sample Imbalance

This presentation discusses the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios and proposes a semantic‑aware active‑learning framework that combines uncertainty, graph structure, prototype diversity, and double‑channel information alignment to improve node classification performance.

Graph Neural Networksactive learninggraph data

0 likes · 18 min read

Semantic‑Aware Active Learning on Graph Data for Risk Control: Tackling Sample Imbalance

DataFunTalk

Sep 21, 2023 · Artificial Intelligence

Active Learning and Sample Imbalance in Graph Data for Risk Control

This presentation explores the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios, proposing semantic‑aware active learning and prototype‑driven sampling strategies to improve node classification performance on imbalanced graph datasets.

Graph Neural Networksactive learninggraph data

0 likes · 16 min read

Active Learning and Sample Imbalance in Graph Data for Risk Control

Amap Tech

Jun 8, 2023 · Fundamentals

The Essence of Learning: Active vs. Passive Approaches and Their Impacts

Learning enriches knowledge but, when pursued passively or excessively, can crowd out personal thought, stifle creativity, and make thinking rigid, so a healthy approach blends active, experience‑based insight, selective classic sources, and continual self‑reflection to turn knowledge into genuine understanding.

active learningcognitioncritical thinking

0 likes · 36 min read

The Essence of Learning: Active vs. Passive Approaches and Their Impacts

58 Tech

May 11, 2023 · Artificial Intelligence

Stella Data Annotation Platform: Design, Architecture, and AI‑Assisted Labeling

The article details the design and implementation of the Stella data annotation SaaS platform at 58.com, covering its background, evolution, modular architecture, annotation capabilities across text, image, audio, and video, AI‑assisted labeling, storage solutions, quality and efficiency management, as well as localization and licensing considerations.

AI PlatformQuality ManagementSystem Architecture

0 likes · 21 min read

Stella Data Annotation Platform: Design, Architecture, and AI‑Assisted Labeling

NetEase Cloud Music Tech Team

Jan 10, 2023 · Artificial Intelligence

Sentiment Classification and Topic Clustering for NetEase Cloud Music Comments

To boost NetEase Cloud Music’s comment handling, the authors combine active‑learning‑driven relabeling, domain‑specific MLM pretraining, contrastive‑learning‑based sample expansion, and multi‑task BERT sharing to raise sentiment‑classification precision and recall above 90 % and double moderation clean‑rate, while employing prompt‑generated story themes, IP‑focused classifiers, and hot‑word aggregation for effective short‑text topic clustering and scalable, theme‑aware distribution.

NLPPretrainingactive learning

0 likes · 10 min read

Sentiment Classification and Topic Clustering for NetEase Cloud Music Comments

Zhuanzhuan Tech

Aug 17, 2022 · Artificial Intelligence

Designing a Scalable Image Classification System for Prohibited Item Detection in a Second‑hand E‑commerce Platform

This article describes how a second‑hand e‑commerce company built a fast, modular image‑classification pipeline using small binary classifiers, efficientNet‑b0, and active‑learning‑driven data annotation to detect prohibited items while keeping inference latency under 200 ms and reducing labeling costs dramatically.

AIModel Architectureactive learning

0 likes · 10 min read

Designing a Scalable Image Classification System for Prohibited Item Detection in a Second‑hand E‑commerce Platform

DataFunSummit

Jun 23, 2022 · Artificial Intelligence

Unlocking Data Potential: Automatic Data Augmentation, Denoising, Active Learning, and Data Splitting

The talk explains how to maximize the value of training data by exploring background on model generalization, automatic data augmentation techniques, denoising strategies, active learning for selecting unlabeled samples, and robust data splitting methods, offering practical guidelines for AI practitioners.

AIactive learningautomatic denoise

0 likes · 16 min read

Unlocking Data Potential: Automatic Data Augmentation, Denoising, Active Learning, and Data Splitting

360 Smart Cloud

Sep 13, 2021 · Artificial Intelligence

Active Learning: Concepts, Workflow, Strategies, and Evaluation Metrics

Active learning addresses the high cost of labeling data by iteratively selecting the most informative unlabeled samples for annotation, thereby reducing labeling effort while achieving target model performance, and the article explains its fundamentals, relationship to supervised and semi‑supervised learning, common selection strategies, hybrid methods, and evaluation metrics.

Labeling Cost ReductionQuery by CommitteeUncertainty Sampling

0 likes · 7 min read

Active Learning: Concepts, Workflow, Strategies, and Evaluation Metrics

58 Tech

Aug 10, 2021 · Artificial Intelligence

Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data

This article presents a comprehensive study on extracting semantic tags from 58.com voice data, detailing the use of active learning to address cold‑start problems, comparing keyword matching, XGBoost, TextCNN, CRNN, and an improved Wide&Deep model, and demonstrating significant reductions in labeling effort and superior F1 scores across multiple experiments.

CRNNText Classificationactive learning

0 likes · 15 min read

Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data

DataFunTalk

Sep 13, 2020 · Artificial Intelligence

Active Learning: Concepts, Query Strategies, and Applications

Active Learning is a machine learning approach that reduces labeling costs by iteratively selecting the most informative samples for human annotation, using various query strategies such as uncertainty sampling, query-by-committee, expected model change, and density-weighted methods, applicable to domains like image classification, security risk control, and anomaly detection.

Labeling Cost ReductionQuery StrategiesUncertainty Sampling

0 likes · 15 min read

Active Learning: Concepts, Query Strategies, and Applications

DataFunTalk

Apr 5, 2020 · Artificial Intelligence

WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation

This article describes a WeChat hotspot mining platform that integrates multiple data sources, builds quality and prediction models, employs advanced clustering and multi‑granular text matching techniques, and uses generative active learning to efficiently discover, predict, and present news hotspots for users.

Text ClassificationWeChatactive learning

0 likes · 17 min read

WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation

Model Perspective

Sep 8, 2018 · Fundamentals

How the STEM SOS Model Transforms Classroom Engagement and Learning

The article explains the STEM SOS teaching approach—an interactive, project‑based model that blends concept lectures, hands‑on experiments, student‑led presentations, and multimedia resources—to boost student participation, confidence, and deep understanding of scientific concepts.

STEM educationactive learninghands‑on teaching

0 likes · 8 min read

How the STEM SOS Model Transforms Classroom Engagement and Learning

AntTech

Apr 16, 2018 · Artificial Intelligence

Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine

This article presents an Active PU Learning framework that combines active learning with two‑step positive‑unlabeled learning to improve cash‑out fraud detection in Alipay’s fifth‑generation risk engine, AlphaRisk, achieving three‑fold identification gains over unsupervised methods while reducing labeling costs.

Semi-supervised Learningactive learningfraud detection

0 likes · 14 min read

Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine

Qunar Tech Salon

Aug 13, 2014 · Fundamentals

The Learning Pyramid: Theory, Classroom Application, and Teaching Implications

The article explains the Learning Pyramid theory, details each learning method’s retention rates, and illustrates its practical use through a U.S. classroom transcript, highlighting the benefits of active, multimodal teaching over passive lecture‑based approaches.

active learningeducationlearning pyramid

0 likes · 7 min read

The Learning Pyramid: Theory, Classroom Application, and Teaching Implications