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Mar 21, 2026 · Artificial Intelligence

Can Peer Review Boost Large Language Model Ensembles? Introducing LLM‑PeerReview

This article analyzes the unsupervised LLM‑PeerReview framework, which uses a peer‑review inspired scoring, reasoning, and selection pipeline—including a novel flipped‑triple scoring trick—to combine multiple large language models and achieve significant performance gains over existing ensemble and collaboration baselines.

Flipped Triple ScoringLLM EnsembleModel Scoring

0 likes · 11 min read

Can Peer Review Boost Large Language Model Ensembles? Introducing LLM‑PeerReview

Python Programming Learning Circle

Sep 11, 2025 · Artificial Intelligence

Essential Machine Learning Algorithms: From Linear Regression to DBSCAN

This article provides a comprehensive overview of key machine‑learning algorithms—including supervised methods like linear regression, SVM, Naive Bayes, logistic regression, k‑NN, decision trees, random forests, GBDT, and unsupervised techniques such as k‑means, hierarchical clustering, DBSCAN, and PCA—explaining their principles, strengths, and typical use cases.

AlgorithmsNaive BayesUnsupervised Learning

0 likes · 20 min read

Essential Machine Learning Algorithms: From Linear Regression to DBSCAN

Data Party THU

Aug 8, 2025 · Artificial Intelligence

How Unsupervised Pre‑training Reshapes Visual Cortex Plasticity in Mice

This article reviews a Nature paper that combines virtual‑reality tasks, unsupervised learning experiments, large‑scale neural recordings, and behavioral analysis to reveal how unsupervised pre‑training drives visual‑cortex plasticity and accelerates subsequent task learning in mice.

Unsupervised Learningmouse modelneural plasticity

0 likes · 9 min read

How Unsupervised Pre‑training Reshapes Visual Cortex Plasticity in Mice

Qborfy AI

Jun 26, 2025 · Artificial Intelligence

Unlock Hidden Patterns: A Hands‑On Guide to Unsupervised Learning Techniques

This article explains unsupervised learning by defining its core concepts, comparing clustering, dimensionality reduction, and association techniques, and illustrating each with concrete examples—from restaurant dish grouping and housing decision simplification to convenience‑store product analysis—while offering hands‑on experiments and real‑world case studies such as Amazon, NASA, and 7‑Eleven.

AICase StudiesUnsupervised Learning

0 likes · 5 min read

Unlock Hidden Patterns: A Hands‑On Guide to Unsupervised Learning Techniques

Python Programming Learning Circle

Apr 30, 2025 · Artificial Intelligence

Homemade Machine Learning: Python Implementations of Popular Machine Learning Algorithms with Jupyter Notebook Demos

This article introduces the open‑source Homemade Machine Learning project, which implements popular supervised and unsupervised algorithms from first principles in Python, provides Jupyter Notebook demos, code examples, and step‑by‑step setup instructions for learners who want to understand the mathematics and practice the models.

Jupyter NotebookUnsupervised Learninghomemade algorithms

0 likes · 7 min read

Homemade Machine Learning: Python Implementations of Popular Machine Learning Algorithms with Jupyter Notebook Demos

Python Programming Learning Circle

Jan 22, 2025 · Artificial Intelligence

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

This article provides a clear, illustrated overview of machine learning, explaining its place within artificial intelligence, the main sub‑fields such as supervised and unsupervised learning, classic algorithms, ensemble methods, and practical examples to help beginners grasp core concepts.

Unsupervised Learningclassificationensemble methods

0 likes · 8 min read

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

DataFunSummit

Oct 31, 2024 · Artificial Intelligence

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

This article presents Tencent's research on community recommendation for online games, introducing an adaptive K‑Free community detection algorithm (DAG) to address cold‑start and unknown community count, a constrained large‑scale recommendation method (ComRec), their evaluation metrics, experimental results, and deployment insights.

Tencent GamesUnsupervised Learningcommunity-detection

0 likes · 20 min read

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

Architect

Sep 27, 2024 · Artificial Intelligence

How AI Detects and Diagnoses Anomalies in Ctrip Train Ticket Metrics

This article presents a comprehensive AI‑driven system for automatically detecting anomalies in over 1,000 Ctrip train‑ticket business metrics and pinpointing their root causes, detailing the background, unsupervised algorithms, detection and attribution pipelines, practical results, and future improvements.

AI anomaly detectionCtripRoot Cause Analysis

0 likes · 21 min read

How AI Detects and Diagnoses Anomalies in Ctrip Train Ticket Metrics

Alibaba Cloud Big Data AI Platform

Aug 27, 2024 · Artificial Intelligence

How AI Detects Cluster-Wide Task Slowdowns in Cloud Systems

A new AI‑driven method for detecting cluster‑wide task slowdowns in cloud platforms improves F1 score by 5.3% over state‑of‑the‑art techniques, addressing challenges of composite periodic patterns, training data contamination, and focusing on slowdown anomalies.

Neural NetworksTime SeriesUnsupervised Learning

0 likes · 8 min read

How AI Detects Cluster-Wide Task Slowdowns in Cloud Systems

DataFunTalk

Aug 4, 2024 · Artificial Intelligence

Community Recommendation in Tencent Games: Adaptive K‑Free Community Detection and Constrained Large‑Scale Community Recommendation (ComRec)

This article presents Tencent's research on community recommendation for online games, covering the motivation behind recommending player groups, the challenges of cold‑start and data sparsity, the adaptive K‑Free community detection algorithm (DAG) with joint structural‑semantic learning, the constrained large‑scale ComRec algorithm, extensive offline and online experiments, and practical deployment insights.

Tencent GamesUnsupervised Learningcommunity recommendation

0 likes · 20 min read

Ops Development & AI Practice

Jul 3, 2024 · Artificial Intelligence

Supervised vs Unsupervised Learning: Core Principles, Algorithms, and Real‑World Uses

This article explains the fundamental concepts, key characteristics, common algorithms, and typical application scenarios of supervised and unsupervised machine learning, helping readers choose the appropriate method for their specific problems.

ApplicationsUnsupervised Learningmachine learning

0 likes · 5 min read

Supervised vs Unsupervised Learning: Core Principles, Algorithms, and Real‑World Uses

Ops Development & AI Practice

Jun 22, 2024 · Artificial Intelligence

Machine Learning Demystified: Traditional Algorithms vs Neural Networks

Machine learning, a core AI discipline, encompasses traditional algorithms like supervised, unsupervised, and reinforcement learning as well as neural network models such as CNNs, RNNs, GANs, and VAEs, each with distinct principles, strengths, and typical application scenarios.

Deep LearningNeural NetworksTraditional Algorithms

0 likes · 10 min read

Machine Learning Demystified: Traditional Algorithms vs Neural Networks

Alibaba Cloud Big Data AI Platform

Jun 5, 2024 · Artificial Intelligence

LARA: A Light, Anti‑Overfitting Retraining Method for Unsupervised Time‑Series Anomaly Detection

The LARA approach, presented at WWW2024, offers a lightweight, anti‑overfitting retraining solution for unsupervised time‑series anomaly detection in cloud services, achieving state‑of‑the‑art accuracy with minimal new data and dramatically reducing training overhead.

Cloud AITime SeriesUnsupervised Learning

0 likes · 5 min read

LARA: A Light, Anti‑Overfitting Retraining Method for Unsupervised Time‑Series Anomaly Detection

Python Programming Learning Circle

May 10, 2024 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

This article compiles and explains a variety of common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing algorithm descriptions, workflow steps, advantages, limitations, and ready‑to‑run Python code snippets for each approach.

PythonUnsupervised Learninganomaly detection

0 likes · 23 min read

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

Model Perspective

Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

Discriminative ModelsGenerative ModelsUnsupervised Learning

0 likes · 13 min read

Master the Three Machine Learning Types and Model Paradigms

Ctrip Technology

Oct 19, 2023 · Artificial Intelligence

Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics

This article presents an AI‑driven system that automatically detects anomalies in over 1,000 Ctrip train‑ticket business metrics using six unsupervised algorithms and locates their root causes through a hard‑voting ensemble of four specialized methods, demonstrating practical results and future enhancements.

CtripRoot Cause AnalysisTime Series

0 likes · 18 min read

Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics

JD Tech

Sep 12, 2023 · Fundamentals

Community Detection Algorithms: Concepts, Types, and Classic Methods

This article introduces community detection as a fundamental graph algorithm, explains its basic concepts and types, compares it with clustering, discusses evaluation metrics like modularity, and reviews classic methods such as Louvain, node2vec‑based approaches, and the information‑theoretic Infomap algorithm.

InfomapUnsupervised Learningcommunity-detection

0 likes · 13 min read

Community Detection Algorithms: Concepts, Types, and Classic Methods

Network Intelligence Research Center (NIRC)

Jul 29, 2023 · Artificial Intelligence

Getting Started with GPT: How Generative Pre‑Training and Discriminative Fine‑Tuning Work

This article explains GPT's two‑stage learning—unsupervised generative pre‑training on large raw corpora followed by discriminative fine‑tuning on labeled tasks—detailing the underlying Transformer decoder architecture, loss functions, and task‑specific input transformations.

Fine-tuningGPTGenerative Pre‑Training

0 likes · 5 min read

Getting Started with GPT: How Generative Pre‑Training and Discriminative Fine‑Tuning Work

Rare Earth Juejin Tech Community

Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

AlgorithmsLoss FunctionsUnsupervised Learning

0 likes · 35 min read

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Graph Neural NetworkUnsupervised Learninganomaly detection

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

Architect

May 24, 2023 · Artificial Intelligence

A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications

Graph Neural Networks (GNNs) have become a research hotspot, and this article provides an intuitive overview of classic GNN models such as GCN, GraphSAGE, GAT, graph auto‑encoders, and DiffPool, discussing their architectures, advantages, limitations, and experimental results across various benchmark datasets.

DiffPoolGATGCN

0 likes · 18 min read

A Comprehensive Overview of Graph Neural Networks: Models, Techniques, and Applications

DataFunSummit

Feb 19, 2023 · Artificial Intelligence

Intelligent Writing Assistant: TexSmart and Effidit Systems, Multi‑Level Unsupervised Text Rewriting, and the New ParaScore Evaluation Metric

This article presents Tencent AI Lab's intelligent writing assistant, detailing the TexSmart text‑understanding platform, the Effidit writing‑assistant features, a multi‑level controllable unsupervised text‑rewriting method, and a novel ParaScore metric that jointly measures semantic similarity and diversity for paraphrase evaluation.

AI writingEvaluation MetricsNLP

0 likes · 14 min read

Intelligent Writing Assistant: TexSmart and Effidit Systems, Multi‑Level Unsupervised Text Rewriting, and the New ParaScore Evaluation Metric

DataFunSummit

Feb 6, 2023 · Artificial Intelligence

A Minimalist White‑Box Unsupervised Learning Method Using Sparse Manifold Transform

A recent paper by Prof. Ma Yi and Turing‑Award winner Yann LeCun introduces a simple, interpretable unsupervised learning approach that combines sparse coding, manifold learning, and slow feature analysis, achieving near‑state‑of‑the‑art performance on MNIST, CIFAR‑10, and CIFAR‑100 without data augmentation or extensive hyper‑parameter tuning.

AIDeep LearningUnsupervised Learning

0 likes · 8 min read

A Minimalist White‑Box Unsupervised Learning Method Using Sparse Manifold Transform

DataFunSummit

Feb 4, 2023 · Artificial Intelligence

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

This article introduces deep learning as a powerful AI technique, explains its core algorithms—including supervised, unsupervised, and semi‑supervised approaches—and provides concrete examples such as CNN, RNN, autoencoders, GAN, self‑supervised and transfer learning, illustrated with visual demos.

AIDeep LearningGAN

0 likes · 6 min read

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

Model Perspective

Jan 8, 2023 · Artificial Intelligence

Unlock Hidden Patterns: A Deep Dive into Unsupervised Learning Techniques

This article introduces unsupervised learning, covering its motivation, Jensen's inequality, key clustering methods such as EM, k‑means, hierarchical clustering, evaluation metrics, and dimensionality‑reduction techniques like PCA and ICA, providing clear explanations and illustrative diagrams.

EM algorithmICAK-Means

0 likes · 8 min read

Unlock Hidden Patterns: A Deep Dive into Unsupervised Learning Techniques

Model Perspective

Nov 8, 2022 · Artificial Intelligence

Mastering K-Means: How Distance-Based Clustering Works and How to Implement It

This article explains the fundamentals of the K-means clustering algorithm, describing its distance‑based similarity principle, the objective of minimizing squared error, and a step‑by‑step iterative procedure—including random centroid initialization, assignment, centroid recomputation, and convergence criteria.

Unsupervised Learningalgorithmclustering

0 likes · 3 min read

Mastering K-Means: How Distance-Based Clustering Works and How to Implement It

JD Cloud Developers

Nov 7, 2022 · Artificial Intelligence

Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion

This article presents a threshold‑free anomaly detection framework for streaming time series that combines an LSTM‑based baseline module with an unsupervised detection module, detailing the architecture, training process, data preprocessing, and experimental results that demonstrate superior accuracy and F1 scores.

Deep LearningLSTMTime Series

0 likes · 15 min read

Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion

Model Perspective

Nov 5, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms and How They Work

This comprehensive guide walks you through the most popular machine learning algorithms, explaining how they are classified by learning style and problem type, and highlighting key examples from supervised, unsupervised, deep learning, ensemble, and many other algorithm families.

Deep LearningUnsupervised Learningsupervised learning

0 likes · 11 min read

Explore the Most Popular Machine Learning Algorithms and How They Work

Model Perspective

Oct 26, 2022 · Artificial Intelligence

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

This article categorizes machine learning algorithms into supervised, unsupervised, and reinforcement learning, then details ten common algorithms—including linear regression, logistic regression, decision trees, SVM, Naive Bayes, K‑NN, K‑means, random forest, and dimensionality reduction—accompanied by clear Python code examples and illustrative diagrams.

AlgorithmsPythonUnsupervised Learning

0 likes · 14 min read

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

MaGe Linux Operations

Sep 8, 2022 · Artificial Intelligence

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

This tutorial introduces unsupervised clustering, explains its purpose, and walks through installing scikit‑learn and implementing ten popular clustering algorithms—including AffinityPropagation, Agglomerative, BIRCH, DBSCAN, K‑Means, Mini‑Batch K‑Means, MeanShift, OPTICS, Spectral Clustering, and Gaussian Mixture—complete with code examples and visualizations.

Unsupervised Learningclusteringdata mining

0 likes · 27 min read

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

HomeTech

Sep 8, 2022 · Artificial Intelligence

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

This article presents a comprehensive overview of concept tag mining for recommendation systems, describing unsupervised pattern‑matching, semi‑supervised AutoPhase, and supervised NER approaches, analyzing their advantages and drawbacks, and offering practical solutions to tag duplication and quality issues.

NERNLPSemi-supervised Learning

0 likes · 11 min read

Concept Tag Mining for Recommendation Systems: Methods, Challenges, and Solutions

Model Perspective

Aug 5, 2022 · Artificial Intelligence

What Are the Essential Steps and Types of Machine Learning?

Machine learning involves five core steps—from data collection and preparation to model training, evaluation, and improvement—while encompassing supervised, unsupervised, and reinforcement learning methods, each with distinct algorithms and real-world applications across finance, healthcare, and retail.

ApplicationsUnsupervised Learningmachine learning

0 likes · 7 min read

What Are the Essential Steps and Types of Machine Learning?

Model Perspective

Aug 3, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

This article provides a thorough overview of the most widely used machine learning algorithms, classifying them by learning style and problem type, and highlighting popular methods such as supervised, unsupervised, semi‑supervised, regression, instance‑based, regularization, decision‑tree, Bayesian, clustering, association rule, neural network, deep learning, dimensionality‑reduction, and ensemble techniques.

AlgorithmsDeep LearningUnsupervised Learning

0 likes · 10 min read

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

MaGe Linux Operations

Jul 29, 2022 · Artificial Intelligence

Master 10 Popular Clustering Algorithms in Python with Scikit‑Learn

This tutorial introduces clustering, explains why no single algorithm fits all data, and provides step‑by‑step Python examples using scikit‑learn for ten popular unsupervised learning methods, complete with code snippets and visualizations to illustrate results.

PythonUnsupervised Learningclustering

0 likes · 24 min read

AntTech

Jul 12, 2022 · Artificial Intelligence

AI Visual Anti‑Fraud Model Battles QR Code Abuse in the Beverage Industry

The article describes how Ant Group's AI visual anti‑fraud system, built by vision engineers, combats large‑scale QR‑code fraud targeting beverage bottle caps, detailing the black‑gray industry's tactics, the model's rapid detection capabilities, continuous unsupervised learning upgrades, and its broader applications in remote‑sensing and risk management.

AIImage ProcessingQR code

0 likes · 13 min read

AI Visual Anti‑Fraud Model Battles QR Code Abuse in the Beverage Industry

DaTaobao Tech

Jul 1, 2022 · Artificial Intelligence

Deep Generative Projection for High‑Fidelity Virtual Try‑On

The paper presents Deep Generative Projection (DGP), a virtual‑try‑on system that learns a realistic dressing distribution from unpaired images with StyleGAN, projects coarse garment‑human alignments into its latent space, refines details, and achieves higher fidelity and robustness than supervised SOTA methods without needing paired data.

Computer VisionUnsupervised Learninggenerative adversarial network

0 likes · 13 min read

Deep Generative Projection for High‑Fidelity Virtual Try‑On

21CTO

Jun 26, 2022 · Artificial Intelligence

Can Babies Teach Us to Build the Next Generation of AI?

Researchers at Trinity College Dublin propose new AI guidelines inspired by infant learning, arguing that babies' experiential, unsupervised learning can overcome current machine learning limitations, and outlining three principles to help develop more efficient, data‑light AI systems.

AIUnsupervised Learninginfant learning

0 likes · 4 min read

Can Babies Teach Us to Build the Next Generation of AI?

Model Perspective

Jun 4, 2022 · Artificial Intelligence

Master K-means Clustering: How the Algorithm Finds Compact Groups

K-means is a classic distance‑based clustering algorithm that iteratively partitions data into k compact, well‑separated groups by minimizing the sum of squared errors, using random centroid initialization and heuristic updates until convergence, making it a fundamental tool in AI and data analysis.

K-MeansUnsupervised Learningalgorithm

0 likes · 3 min read

Master K-means Clustering: How the Algorithm Finds Compact Groups

DataFunSummit

May 23, 2022 · Artificial Intelligence

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

This article explores how graph machine learning can be leveraged for intelligent anti‑fraud, covering business background, common fraud models and graph algorithm principles, practical deployment of graph algorithms, challenges in fraud modeling, and future research directions.

Graph Machine LearningRisk ModelingUnsupervised Learning

0 likes · 20 min read

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

NetEase Game Operations Platform

May 9, 2022 · Operations

Intelligent Log Classification and Anomaly Detection: Design and Implementation

This article presents a two‑stage streaming log classification system using an improved prefix‑tree and longest‑common‑subsequence algorithms, along with a statistical unsupervised anomaly detection method that leverages chi‑square aggregation and box‑plot scoring to reduce false alarms and accelerate template convergence.

LCS algorithmOperationsUnsupervised Learning

0 likes · 11 min read

Intelligent Log Classification and Anomaly Detection: Design and Implementation

Python Programming Learning Circle

Apr 14, 2022 · Artificial Intelligence

Top Clustering Algorithms in Python with scikit-learn: A Comprehensive Tutorial

This tutorial explains clustering as an unsupervised learning task, outlines why no single algorithm fits all data, and provides step‑by‑step Python code using scikit‑learn to install the library, generate synthetic datasets, and apply ten popular clustering algorithms with visualizations.

PythonUnsupervised Learningclustering

0 likes · 21 min read

Top Clustering Algorithms in Python with scikit-learn: A Comprehensive Tutorial

Kuaishou Tech

Feb 25, 2022 · Artificial Intelligence

Reference‑Guided Image Synthesis Assessment (RISA): Unsupervised Training for Single‑Image Quality Evaluation

The paper presents RISA, a reference‑guided image synthesis assessment model that learns to score the quality of a single generated image without human‑labeled data by leveraging GAN intermediate outputs, pixel‑wise interpolation, multiple binary classifiers, and contrastive learning, achieving results comparable to human perception and earning an AAAI 2022 oral presentation.

AIGANRISA

0 likes · 8 min read

Reference‑Guided Image Synthesis Assessment (RISA): Unsupervised Training for Single‑Image Quality Evaluation

DataFunSummit

Jan 8, 2022 · Artificial Intelligence

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve graph neural network performance without requiring task labels.

Graph RepresentationRobustnessUnsupervised Learning

0 likes · 15 min read

Graph Information Bottleneck and AD‑GCL: Enhancing Graph Representation Learning and Robustness

58 Tech

Dec 14, 2021 · Artificial Intelligence

Unsupervised Community Detection for Black‑Market Identification Using the Louvain Algorithm

This article presents an unsupervised community‑discovery approach based on the Louvain algorithm to identify black‑market accounts, describing the threat landscape, system architecture, algorithmic principles, optimizations, experimental results, and future directions for improving risk detection in large‑scale online services.

Louvain AlgorithmRisk analysisUnsupervised Learning

0 likes · 10 min read

Unsupervised Community Detection for Black‑Market Identification Using the Louvain Algorithm

Baobao Algorithm Notes

Dec 12, 2021 · Artificial Intelligence

Why the AAAI22 Re‑ID Paper Leaks Data and a Simpler Alternative Beats It

The author examines the AAAI 2022 paper “Mind Your Clever Neighbours,” reveals that it exploits a data‑leak in unsupervised person re‑identification, critiques the unnecessary Graph Correlation Learning step, and demonstrates a much simpler averaging method that yields superior results.

Unsupervised Learningdata leakagegraph correlation learning

0 likes · 6 min read

Why the AAAI22 Re‑ID Paper Leaks Data and a Simpler Alternative Beats It

DataFunTalk

Dec 11, 2021 · Artificial Intelligence

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

This article introduces graph representation learning, explains the Graph Information Bottleneck (GIB) framework for obtaining robust graph embeddings, and presents AD‑GCL, a contrastive learning method that leverages GIB principles to improve robustness and performance without requiring task labels.

RobustnessUnsupervised Learningcontrastive learning

0 likes · 16 min read

Graph Information Bottleneck (GIB) and AD‑GCL: Enhancing Graph Representation Learning and Robustness

DataFunTalk

Nov 20, 2021 · Artificial Intelligence

Intelligent Pre‑Loan Risk Control: Multi‑Loop Feedback Model, AutoML Controller Selection, Unsupervised Feature Extraction, and Metric Design

The article presents Akulaku's intelligent pre‑loan risk control framework, detailing a multi‑loop feedback control model, AutoML‑driven controller selection, unsupervised feature extraction techniques, and a comprehensive metric quantification system to improve stability, steady‑state, and dynamic responses of financial risk management.

AutoMLUnsupervised Learningfeedback control

0 likes · 12 min read

Intelligent Pre‑Loan Risk Control: Multi‑Loop Feedback Model, AutoML Controller Selection, Unsupervised Feature Extraction, and Metric Design

DataFunTalk

Sep 18, 2021 · Artificial Intelligence

Unsupervised Algorithms for Fraud Detection in Huya's Risk Control System

This article presents Huya's exploration of unsupervised learning techniques for risk control, detailing business risk scenarios, black‑market attack vectors, limitations of traditional defenses, and the design, implementation, and evaluation of graph‑based and density‑based clustering methods to automatically discover and mitigate fraudulent user groups.

AIHuyaUnsupervised Learning

0 likes · 11 min read

Unsupervised Algorithms for Fraud Detection in Huya's Risk Control System

Baidu Intelligent Testing

Sep 14, 2021 · Information Security

Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding

This article presents a community‑encoding approach that leverages large‑scale graph‑embedding (GraphSAGE) and asynchronous near‑real‑time engineering to identify and measure unknown black‑gray market attacks with higher accuracy and flexibility than traditional graph‑mining methods.

GraphSAGEUnsupervised Learningblack‑gray market

0 likes · 15 min read

Community Encoding Based Detection of Black and Gray Market Attacks Using Graph Embedding

ByteFE

Aug 2, 2021 · Artificial Intelligence

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

This article provides a beginner‑friendly overview of artificial intelligence, its relationship with machine learning, the four major learning paradigms—supervised, unsupervised, semi‑supervised and reinforcement learning—along with a historical sketch of neural networks, their training workflow, loss functions, back‑propagation, and parameter‑update mechanisms, while also containing a brief recruitment notice.

Deep LearningNeural NetworksUnsupervised Learning

0 likes · 18 min read

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

Kuaishou Large Model

May 13, 2021 · Artificial Intelligence

How Regressive Domain Adaptation Boosts Unsupervised Keypoint Detection

This article reviews the CVPR2021 paper on Regressive Domain Adaptation (RegDA) for unsupervised keypoint detection, explaining its motivation, novel adversarial regression framework, sparse output-space modeling, min‑min training strategy, extensive experiments, and the resulting performance gains across multiple datasets.

Computer VisionUnsupervised Learningdomain adaptation

0 likes · 13 min read

How Regressive Domain Adaptation Boosts Unsupervised Keypoint Detection

58 Tech

May 10, 2021 · Information Security

Marketing Anti‑Fraud Algorithm Framework and Practice at 58.com

This article details the design, implementation, and evaluation of a multi‑layer anti‑fraud system for 58.com’s marketing activities, covering data and feature engineering, unsupervised and supervised models, graph‑based community detection, and semi‑supervised graph neural networks, with empirical results demonstrating their effectiveness.

Graph Neural NetworkMarketingUnsupervised Learning

0 likes · 18 min read

Marketing Anti‑Fraud Algorithm Framework and Practice at 58.com

Baidu Geek Talk

Apr 16, 2021 · Artificial Intelligence

Large-Scale Short Text Clustering System Design and Practice at Baidu Search

At Baidu Search, a large‑scale short‑text clustering system was built using multi‑level semantic splitting, fine‑grained aggregation and error‑correction, evolving from v1.0 to v2.0, and now clusters 100 million queries with 95 % accuracy and 80 % recall within three days.

Unsupervised Learningmulti-level splittingsearch query processing

0 likes · 19 min read

Large-Scale Short Text Clustering System Design and Practice at Baidu Search

DataFunTalk

Mar 9, 2021 · Artificial Intelligence

Introduction to Common Machine Learning Algorithms with Python Implementations

This article introduces the three main categories of machine learning—supervised, unsupervised, and reinforcement learning—detailing common algorithms such as Linear Regression, Logistic Regression, Naive Bayes, K‑Nearest Neighbors, Decision Trees, Random Forests, SVM, K‑Means, and PCA, and provides concise Python code examples using scikit‑learn for each.

PythonUnsupervised Learningmachine learning

0 likes · 18 min read

Introduction to Common Machine Learning Algorithms with Python Implementations

Architects' Tech Alliance

Jan 29, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

This article provides a detailed introduction to machine learning, covering its definition, learning modes such as supervised, unsupervised and reinforcement learning, shallow versus deep learning, the full industry chain from AI chips to cloud and big‑data services, and the major open‑source frameworks and platforms driving the field.

AI chipsBig DataUnsupervised Learning

0 likes · 11 min read

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

MaGe Linux Operations

Sep 9, 2020 · Artificial Intelligence

Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough

This comprehensive tutorial introduces machine learning fundamentals, its history, differences from traditional programming, key characteristics, and why Python is the preferred language, then explores supervised, unsupervised, and reinforcement learning, popular algorithms, detailed K‑Nearest Neighbors examples for classification and regression, and the essential steps to build and evaluate models.

PythonUnsupervised LearningkNN

0 likes · 21 min read

Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough

DataFunTalk

Aug 19, 2020 · Artificial Intelligence

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

The article presents a comprehensive overview of e‑commerce fraud, especially brush‑order schemes, and introduces the Fraudar algorithm—a graph‑based unsupervised method that leverages bipartite network analysis, global suspiciousness metrics, priority‑tree optimization, and collaborative supervised training to efficiently identify dense fraudulent sub‑graphs.

FraudarUnsupervised Learningbipartite graph

0 likes · 15 min read

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

JD Tech Talk

Aug 7, 2020 · Information Security

Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks

The article explains how e‑commerce fraud such as fake order brushing can be modeled as a bipartite transaction network and tackled with the Fraudar algorithm, which iteratively removes low‑suspicion nodes using a global suspiciousness metric and priority‑tree structures to uncover dense suspicious sub‑graphs.

Unsupervised Learningbipartite graphe‑commerce

0 likes · 14 min read

Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks

Youku Technology

Apr 16, 2020 · Artificial Intelligence

Multimodal Video Classification: Image Feature Improvements and System Insights

The talk presents Alibaba’s hierarchical video‑category system and a multimodal classification pipeline—leveraging EfficientNet, NeXtVLAD fusion, attention‑dropping augmentation, and MoCo contrastive learning—that together boost cold‑start recall by 43%, improve program classification over 20%, and set the stage for larger models and advanced unsupervised methods.

AIEfficientNetUnsupervised Learning

0 likes · 17 min read

Multimodal Video Classification: Image Feature Improvements and System Insights

dbaplus Community

Apr 6, 2020 · Databases

How AI‑Driven Intelligent Ops Transform Database Management in Banking

This article examines the severe time‑critical pain points of bank database operations, explains why AI‑based intelligent ops are needed, describes the platform architecture, unsupervised algorithms (3σ, Isolation Forest, DBSCAN, Pearson, Apriori), and presents a real‑world case study that demonstrates anomaly detection, root‑cause analysis, and practical optimization recommendations.

Database operationsPythonRoot Cause Analysis

0 likes · 23 min read

How AI‑Driven Intelligent Ops Transform Database Management in Banking

DataFunTalk

Mar 30, 2020 · Artificial Intelligence

Enhancing Multimodal Video Classification with Improved Image Features and Category System

This article presents a comprehensive overview of Alibaba Entertainment's category system and multimodal video classification algorithm, detailing the construction of a high‑accuracy hierarchical taxonomy, improvements to image feature extraction using EfficientNet and data augmentation, unsupervised training techniques, experimental results, practical pitfalls, and future research directions.

AIUnsupervised Learningcategory system

0 likes · 17 min read

Enhancing Multimodal Video Classification with Improved Image Features and Category System

Python Programming Learning Circle

Mar 6, 2020 · Artificial Intelligence

Introduction to Machine Learning Concepts: Data, Features, Labels, Training, and Common Algorithms

This article provides a beginner-friendly overview of machine learning fundamentals, covering the definition of data, the distinction between features and labels, types of features, dimensionality, training and test datasets, normalization, supervised and unsupervised learning methods, algorithm selection, development workflow, and recommended Python libraries such as NumPy.

Unsupervised Learningdata preprocessingfeatures

0 likes · 12 min read

Introduction to Machine Learning Concepts: Data, Features, Labels, Training, and Common Algorithms

ITPUB

Jan 14, 2020 · Artificial Intelligence

Top 2019 AI Papers Loved by Reddit Users: Key Insights and Links

A curated collection of Reddit‑highlighted 2019 AI research papers, covering theoretical advances, computer‑vision breakthroughs, unsupervised learning methods, and time‑series forecasting, with summaries, key contributions, and direct links to each paper.

AIComputer VisionMeta Learning

0 likes · 6 min read

Top 2019 AI Papers Loved by Reddit Users: Key Insights and Links

Alibaba Cloud Developer

Aug 15, 2019 · Artificial Intelligence

How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training

This article introduces Auto Risk, a deep‑learning risk model for behavior‑sequence data that leverages unsupervised pre‑training with proxy tasks, details its convolution‑attention encoder, demonstrates significant gains across multiple business scenarios, and highlights its strong small‑sample and analogy capabilities.

Deep LearningRisk ModelingUnsupervised Learning

0 likes · 20 min read

How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training

Alibaba Cloud Developer

Jul 30, 2019 · Artificial Intelligence

Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences

This article introduces Auto Risk, a behavior‑sequence deep‑learning framework that uses unsupervised pre‑training with proxy tasks to learn universal feature representations from massive unlabeled data, achieving significant gains in risk‑control scenarios, improving AUC, supporting multi‑scene generalization and small‑sample learning.

Deep LearningRisk ModelingUnsupervised Learning

0 likes · 20 min read

Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences

AntTech

Jul 21, 2019 · Artificial Intelligence

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

At SIGIR 2019 in Paris, Alipay presented two AI research papers—one applying reinforcement learning to predict user intent in customer‑service bots and another introducing the unsupervised QUEST method that builds noisy quasi‑knowledge graphs for answering complex multi‑document questions.

AIKnowledge GraphUnsupervised Learning

0 likes · 5 min read

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

Hulu Beijing

Apr 10, 2019 · Artificial Intelligence

Designing Deep Learning Models for Item Similarity in Recommendation Systems

This article explains how to build both unsupervised and supervised deep‑learning models that compute item similarity from user behavior, covering prod2vec embeddings, skip‑gram architectures, loss function design, and practical training steps for modern recommender systems.

Deep LearningRecommendation SystemsUnsupervised Learning

0 likes · 8 min read

Designing Deep Learning Models for Item Similarity in Recommendation Systems

Alibaba Cloud Developer

Nov 21, 2018 · Artificial Intelligence

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

International credit card fraud, a growing threat, can be more effectively identified by applying unsupervised autoencoder models, which outperform traditional rule‑based systems by tripling recall and increasing accuracy by 40%, while reducing maintenance costs and adapting to new fraud patterns.

AutoencoderUnsupervised Learninganomaly detection

0 likes · 9 min read

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

Tencent Cloud Developer

Oct 23, 2018 · Artificial Intelligence

Demystifying AI, Machine Learning, and Deep Learning

The article clarifies that artificial intelligence encompasses machine learning, which in turn includes deep learning, and uses real‑world examples—from fraud detection and customer clustering to image recognition and language translation—to illustrate how these data‑driven models learn patterns, make predictions, and transform many industries.

Data ScienceDeep LearningUnsupervised Learning

0 likes · 12 min read

Demystifying AI, Machine Learning, and Deep Learning

DataFunTalk

Oct 17, 2018 · Artificial Intelligence

Design Principles for AI‑Driven Anti‑Fraud Systems

The article outlines Tongdun Technology's anti‑fraud challenges, presents their AI‑based detection solutions, and details design principles—including early warning, multi‑feature analysis, and human‑machine collaboration—to build a robust, multi‑layered fraud prevention framework.

AI designRisk DetectionUnsupervised Learning

0 likes · 10 min read

Design Principles for AI‑Driven Anti‑Fraud Systems

Efficient Ops

Aug 28, 2018 · Operations

How to Detect and Resolve Time‑Series Anomalies in Modern AIOps

This article explains practical approaches for time‑series anomaly detection, multi‑dimensional drill‑down analysis, alarm‑convergence root‑cause analysis, and future AIOps planning, combining statistical methods, unsupervised learning, and supervised models to improve monitoring accuracy and operational efficiency.

OperationsRoot Cause AnalysisUnsupervised Learning

0 likes · 20 min read

How to Detect and Resolve Time‑Series Anomalies in Modern AIOps

21CTO

Jul 22, 2018 · Artificial Intelligence

Can AI Seamlessly Cloak Nudity? Unsupervised Image-to-Image Translation with GANs

Researchers propose an unsupervised GAN-based image-to-image translation method that automatically dresses nude women in bikinis, preserving semantic content while removing sensitive parts, using unpaired datasets and Mask‑RCNN background removal, demonstrating impressive visual results without manual annotation.

GANImage-to-Image TranslationUnsupervised Learning

0 likes · 10 min read

Can AI Seamlessly Cloak Nudity? Unsupervised Image-to-Image Translation with GANs

Alibaba Cloud Developer

Jun 22, 2018 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This beginner-friendly guide walks through core machine‑learning concepts—from data organization and feature design to supervised and unsupervised algorithms such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques—while explaining model evaluation, overfitting, and practical tuning strategies.

Deep LearningModel EvaluationUnsupervised Learning

0 likes · 8 min read

Essential Machine Learning Algorithms Every Beginner Must Know

Alibaba Cloud Developer

Apr 25, 2018 · Artificial Intelligence

How cw2vec Beats Word2Vec: Leveraging Chinese Stroke N‑grams for Superior Word Embeddings

This article introduces cw2vec, a novel Chinese word‑embedding algorithm that exploits stroke‑level subword information, outlines its theoretical foundations, compares it with word2vec, GloVe, CWE and other models on multiple benchmarks, and demonstrates its superior performance across word similarity, analogy, text classification and named‑entity recognition tasks.

Chinese NLPDeep LearningUnsupervised Learning

0 likes · 14 min read

How cw2vec Beats Word2Vec: Leveraging Chinese Stroke N‑grams for Superior Word Embeddings

Architects' Tech Alliance

Mar 9, 2018 · Artificial Intelligence

Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

An in‑depth, visual guide walks readers through the fundamentals of machine learning—distinguishing supervised from unsupervised approaches, explaining dimensionality reduction with PCA, detailing clustering techniques such as hierarchical clustering, K‑Means and DBSCAN, and summarizing core regression and classification algorithms including linear regression, SVM, decision trees, logistic regression, Naïve Bayes, and KNN.

Unsupervised Learningclassificationclustering

0 likes · 11 min read

Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

Hulu Beijing

Feb 8, 2018 · Artificial Intelligence

How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison

This article explains the principles, biological inspiration, network structure, training process, design parameters, and practical differences of Self‑Organizing Maps (SOM), an unsupervised neural network used for clustering, visualization, and feature extraction, and compares it with methods like K‑means.

Neural NetworksSelf-Organizing MapUnsupervised Learning

0 likes · 10 min read

How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison

Qunar Tech Salon

Jan 23, 2018 · Artificial Intelligence

Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull

The article describes how the Super Bus platform leverages unsupervised DBSCAN clustering and a Graham‑scan convex‑hull algorithm, combined with a data‑center and distributed processing framework, to automatically generate compliant service zones that match user demand while improving efficiency and scalability.

DBSCANUnsupervised Learningclustering

0 likes · 8 min read

Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull

AI Large-Model Wave and Transformation Guide

Dec 5, 2017 · Artificial Intelligence

10 Must‑Know Machine Learning Algorithms for Engineers

From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.

AlgorithmsData ScienceModel Evaluation

0 likes · 11 min read

10 Must‑Know Machine Learning Algorithms for Engineers

21CTO

Nov 1, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms: From Decision Trees to ICA Explained

This article introduces the most common machine learning algorithms, covering supervised methods such as decision trees, Naive Bayes, linear regression, logistic regression, SVM, and ensemble techniques, as well as unsupervised approaches like clustering, PCA, SVD, and ICA, with practical examples and visual illustrations.

AlgorithmsUnsupervised Learningmachine learning

0 likes · 10 min read

Essential Machine Learning Algorithms: From Decision Trees to ICA Explained

Alibaba Cloud Developer

Feb 27, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This guide introduces beginners to core machine learning concepts, covering feature design, supervised and unsupervised methods such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting, while explaining model evaluation, overfitting, and practical optimization strategies.

Model EvaluationUnsupervised Learningensemble methods

0 likes · 9 min read

Alibaba Cloud Developer

Oct 8, 2016 · Artificial Intelligence

Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models

An introductory guide for machine‑learning beginners that covers essential algorithms—including perceptrons, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting—explains feature design, model training, evaluation, and practical tips for avoiding under‑ and over‑fitting.

Decision TreesUnsupervised Learningensemble methods

0 likes · 8 min read

Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models