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Apr 23, 2026 · Artificial Intelligence

How Information Shapes Koopman Representations for World Modeling

The paper revisits Koopman representation learning through the lens of the information bottleneck, identifies the exact information needed for stable, long‑term dynamics propagation, proposes an information‑shaped objective combining mutual information and von Neumann entropy, and demonstrates superior prediction accuracy and stability across physical, visual, and graph‑based dynamical systems.

Koopmanmutual informationrepresentation learning

0 likes · 11 min read

How Information Shapes Koopman Representations for World Modeling

Machine Heart

Apr 13, 2026 · Artificial Intelligence

Embracing the Paradigm Shift: A Comprehensive Review of Large‑Model Latent Space

From early 2024 explorations to a 2026 research surge, this review explains how large‑model latent space replaces explicit token‑based processing, outlines its five analytical lenses—foundation, evolution, mechanism, ability, outlook—compares representational properties, details architectural and computational strategies, enumerates new capabilities, and discusses remaining challenges and future directions.

Latent SpaceModel architectureartificial intelligence

0 likes · 20 min read

Embracing the Paradigm Shift: A Comprehensive Review of Large‑Model Latent Space

Machine Learning Algorithms & Natural Language Processing

Mar 26, 2026 · Artificial Intelligence

Can World Models Be Simplified? Two Approaches from LeCun’s Team and Tsinghua

This article reviews two recent papers—LeWorldModel, which uses a minimal JEPA framework to train an end‑to‑end world model from pixels with only two loss terms, and Fast‑WAM, which questions the necessity of test‑time future imagination and achieves comparable performance with a faster inference pipeline.

JEPAModel Predictive Controlrepresentation learning

0 likes · 9 min read

Can World Models Be Simplified? Two Approaches from LeCun’s Team and Tsinghua

HyperAI Super Neural

Mar 11, 2026 · Artificial Intelligence

Bi-TEAM Raises Hemolysis Prediction Accuracy 350% with Unified Biological‑Semantic and Chemical‑Precision Framework

Bi-TEAM, a cross‑scale representation learning framework that injects local chemical variations into a global protein context, outperforms state‑of‑the‑art baselines on ten biochemical datasets, achieving a 350% boost in hemolysis prediction accuracy and a 66% MCC increase under strict scaffold splits.

Bi-TEAMchemical modificationhemolysis prediction

0 likes · 15 min read

Bi-TEAM Raises Hemolysis Prediction Accuracy 350% with Unified Biological‑Semantic and Chemical‑Precision Framework

DataFunTalk

Dec 7, 2025 · Artificial Intelligence

Is the World Model the Key to AGI? Inside the AI Debate

The article examines the chaotic rise of “world models” in AI, tracing their origins from early mental‑model theory to modern representation‑ and generation‑based approaches, and argues that the current hype reflects a broader shift away from large language models toward embodied, physics‑grounded intelligence.

AI researchWorld Modelsgenerative video

0 likes · 13 min read

Is the World Model the Key to AGI? Inside the AI Debate

Data Party THU

Nov 22, 2025 · Artificial Intelligence

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

FreRA introduces a lightweight, plug‑in frequency‑refined augmentation that adaptively refines spectral components to preserve global semantics while injecting variance, dramatically improving contrastive learning performance on time‑series classification, anomaly detection, and transfer learning across multiple benchmark datasets.

Time Seriescontrastive learningdata augmentation

0 likes · 13 min read

How Frequency‑Refined Augmentation Boosts Contrastive Learning for Time‑Series Classification

Bighead's Algorithm Notes

Sep 20, 2025 · Artificial Intelligence

Recent Time-Series Paper Summaries (Sep 13‑19, 2025)

This article summarizes four recent time‑series forecasting papers, covering a universal delay‑embedding foundation model, a dual causal network that leverages exogenous variables, a distribution‑aware alignment plug‑in called TimeAlign, and a shapelet‑based framework for interpretable directional forecasting in noisy financial markets.

Time Seriescausal networkfinancial markets

0 likes · 9 min read

Recent Time-Series Paper Summaries (Sep 13‑19, 2025)

Tencent Advertising Technology

Sep 3, 2025 · Artificial Intelligence

Boosting Ads Revenue: LFM4Ads’ Full‑Representation Multi‑Granular Transfer Raises GMV 2.45%

Tencent's LFM4Ads introduces a full‑representation, multi‑granular knowledge transfer framework that moves user, item, and cross representations from a large foundation model to downstream tasks, achieving up to 2.45% platform GMV uplift across more than ten advertising scenarios.

Knowledge Transferads recommendationfoundation model

0 likes · 12 min read

Boosting Ads Revenue: LFM4Ads’ Full‑Representation Multi‑Granular Transfer Raises GMV 2.45%

Tencent Advertising Technology

Aug 31, 2025 · Artificial Intelligence

LFM4Ads: Full-Representation Multi-Granular Transfer Boosts Ad Recommendation

Tencent's LFM4Ads foundation model introduces a full-representation, multi-granular knowledge transfer framework that moves user, item, and cross representations to downstream tasks, dramatically improving ad recommendation metrics across dozens of business scenarios.

Knowledge TransferLarge-Scale Trainingad recommendation

0 likes · 10 min read

LFM4Ads: Full-Representation Multi-Granular Transfer Boosts Ad Recommendation

Amap Tech

Jul 14, 2025 · Artificial Intelligence

How UPRE Achieves Zero-Shot Domain Adaptation for Object Detection with Unified Prompts

The UPRE paper, presented at ICCV, introduces a multi‑view domain prompt and a unified representation enhancement to enable zero‑shot domain adaptation for object detection, achieving state‑of‑the‑art performance across diverse weather, geographic, and synthetic‑to‑real scenarios.

Computer VisionPrompt engineeringobject detection

0 likes · 10 min read

How UPRE Achieves Zero-Shot Domain Adaptation for Object Detection with Unified Prompts

AI Algorithm Path

Jun 20, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

This article explains contrastive learning for visual language models, covering its definition, four‑step workflow, how to choose positive and negative pairs, the difference between supervised and self‑supervised variants, and why the technique is essential for zero‑shot and cross‑modal capabilities.

Visual-Language Modelscontrastive learningdata augmentation

0 likes · 6 min read

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

DataFunSummit

Aug 10, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Recommendation System Optimization

This article reviews cutting‑edge research on integrating large language models with graph‑based recommendation systems, detailing four key strategies—LLM node embeddings, deep graph‑LLM fusion, model‑driven graph data training, and text‑modal enhancements—while analyzing representation learning, InfoNCE optimization, explainable recommendations, and extensive experimental validation.

InfoNCELLMRecommendation Systems

0 likes · 18 min read

Leveraging Large Language Models for Graph Recommendation System Optimization

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.

Deep LearningOntologyactive 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.

Deep LearningOntologyRecommendation Systems

0 likes · 26 min read

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

DataFunSummit

Mar 23, 2024 · Artificial Intelligence

Graph Neural Networks for Real-World Complex Scenarios

This article presents a comprehensive overview of recent graph neural network research, covering adversarial representation learning for network embedding, block‑model guided GCN, enhanced class‑discriminative GNNs, self‑supervised contrastive GNNs, experimental results, and conclusions, highlighting their significance in real‑world applications.

GCNadversarial learninggraph neural networks

0 likes · 13 min read

Graph Neural Networks for Real-World Complex Scenarios

DataFunSummit

Jul 31, 2023 · Artificial Intelligence

Knowledge Graph based Graph Neural Network Reasoning: From KG Background to GNN for KG and KG for GNN

This article introduces the fundamentals of knowledge graphs, explains how graph neural networks can be adapted for knowledge graph reasoning, presents specialized GNN designs such as CompGCN and RED‑GNN, and discusses experimental results, interpretability, efficiency improvements, and future research directions.

Graph Neural NetworkKG reasoningKnowledge Graph

0 likes · 11 min read

Knowledge Graph based Graph Neural Network Reasoning: From KG Background to GNN for KG and KG for GNN

Network Intelligence Research Center (NIRC)

May 13, 2023 · Artificial Intelligence

A Quick Survey of Recent Time Series Representation Learning Papers

This article reviews the importance and challenges of time‑series data, then summarizes recent contrastive‑learning approaches (TS2Vec, TF‑C) and mask‑reconstruction methods (PatchTST), highlighting their designs, key mechanisms, and future research directions.

PatchTSTTS2VecTime Series

0 likes · 7 min read

A Quick Survey of Recent Time Series Representation Learning Papers

DataFunTalk

Apr 5, 2023 · Artificial Intelligence

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

This article reviews recent developments in causal representation learning, explaining why causal reasoning is essential, describing methods for i.i.d. data, time‑series, and multi‑distribution scenarios, and illustrating applications such as domain adaptation, video analysis, and financial data with numerous examples and visualizations.

causal discoverycausal inferencedomain adaptation

0 likes · 22 min read

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

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

Jul 9, 2022 · Artificial Intelligence

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Examples

This article presents several mature knowledge‑graph application cases, including Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Sage Knowledge Base platform by Fourth Paradigm, and additional examples in recommendation, medical, QA, and power‑industry domains, highlighting methods, challenges, and model designs.

AIGraph Neural NetworkKnowledge Graph

0 likes · 11 min read

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Examples

DataFunSummit

Jul 6, 2022 · Artificial Intelligence

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

This article reviews several mature knowledge‑graph applications, describing Meituan’s large‑scale “Meituan Brain” for lifestyle services, the Fourth Paradigm’s Sage Knowledge Base platform with various representation‑learning models, and additional use cases in recommendation, QA, drug discovery, and power‑grid domains.

AI applicationsGraph Neural NetworkKnowledge Graph

0 likes · 11 min read

Knowledge Graph Application Cases: Meituan Brain, Sage Knowledge Base, and Other Industry Scenarios

DataFunSummit

May 18, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces automated knowledge graph representation learning, covering background, key techniques such as triple‑based, path‑based and subgraph‑based models, AutoML‑driven model search (AutoSF, Interstellar, RED‑GNN), evaluation metrics, and future research directions in AI.

AutoMLEmbeddingKnowledge Graph

0 likes · 21 min read

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

DataFunTalk

May 14, 2022 · Artificial Intelligence

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

The 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse and Imbalanced Data (DLP‑KDD 2022) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a deadline of May 26, 2022 and acceptance notifications by June 20, 2022.

AIDeep LearningSparse Data

0 likes · 5 min read

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

DataFunTalk

May 8, 2022 · Artificial Intelligence

Automated Knowledge Graph Representation Learning: From Triples to Subgraphs

This talk introduces the background, key directions, and model designs for automated knowledge‑graph representation learning, covering triple‑based, path‑based, and subgraph‑based approaches, the role of AutoML in searching optimal bilinear scoring functions, and future research challenges such as scalability, inductive inference, and domain‑specific applications.

AutoMLEmbeddingKnowledge Graph

0 likes · 20 min read

Tencent Cloud Developer

Apr 27, 2022 · Artificial Intelligence

Alignment-Uniformity Representation Learning for Zero-shot Video Classification (AURL)

The AURL framework, presented by Pu Shi, introduces alignment‑uniformity aware representation learning for zero‑shot video classification, achieving up to 28 % top‑1 accuracy gains on UCF101 and HMDB51, and has already boosted business metrics in Tencent’s advertising, search, and video‑channel recommendation systems.

AlignmentComputer VisionDeep Learning

0 likes · 19 min read

Alignment-Uniformity Representation Learning for Zero-shot Video Classification (AURL)

DataFunSummit

Feb 25, 2022 · Artificial Intelligence

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022

The DataFun Summit 2022 Knowledge Graph Forum, held on March 12, presents cutting‑edge research on knowledge graph representation learning, multi‑hop reasoning, temporal KG question answering, and their applications in finance and retail, featuring talks by leading experts from JD, Fourth Paradigm, Stanford, and Meituan.

AI applicationsKnowledge Graphgraph neural networks

0 likes · 9 min read

Knowledge Graph Representation and Reasoning Forum at DataFun Summit 2022

DataFunTalk

Jan 7, 2022 · Artificial Intelligence

Group-Theoretic Self-Supervised Representation Learning (Lecture)

On Jan 7, 2024, BIT’s “Hundred Lectures” will feature Assistant Professor Hanwang Zhang presenting his group‑theoretic self‑supervised representation learning work, including the IP‑IRM method that iteratively partitions data and applies invariant risk minimization to achieve fully disentangled visual features, with the session streamed via Tencent Meeting.

AIgroup theorymachine learning

0 likes · 4 min read

Group-Theoretic Self-Supervised Representation Learning (Lecture)

DataFunSummit

Sep 26, 2021 · Artificial Intelligence

Contrastive Learning and Its Applications in Weibo Content Representation

This article explains the fundamentals of contrastive learning, reviews typical models such as SimCLR, MoCo, SwAV, BYOL, SimSiam and Barlow Twins, and demonstrates how these methods are applied to Weibo text and multimodal (text‑image) representation tasks like hashtag generation and image‑text matching.

NLPWeibocontrastive learning

0 likes · 18 min read

Contrastive Learning and Its Applications in Weibo Content Representation

DataFunSummit

Sep 14, 2021 · Artificial Intelligence

Knowledge Representation Learning for Knowledge Graphs: Business Overview, Algorithms, and Applications

This article presents an overview of Xiaomi's knowledge graph platform, introduces text‑augmented knowledge representation learning methods such as Jointly and DKRL, and details their practical applications in entity linking, entity recommendation, and knowledge graph completion within AI‑driven services.

Knowledge Graphartificial intelligenceentity linking

0 likes · 20 min read

Knowledge Representation Learning for Knowledge Graphs: Business Overview, Algorithms, and Applications

DataFunTalk

Aug 30, 2021 · Artificial Intelligence

Contrastive Learning: Foundations, Typical Models, and Applications to Weibo Content Representation

This article explains the concept of contrastive learning, its relationship to self‑supervised and metric learning, describes key system components and loss functions, reviews major image, NLP and multimodal models such as SimCLR, MoCo, SwAV, BYOL, and demonstrates how contrastive learning is applied to Weibo hashtag generation, similar‑post retrieval, and text‑image matching using CD‑TOM and W‑CLIP models.

AIWeibocontrastive learning

0 likes · 19 min read

Contrastive Learning: Foundations, Typical Models, and Applications to Weibo Content Representation

DataFunTalk

Jun 30, 2021 · Artificial Intelligence

Knowledge Representation Learning for Xiaomi Knowledge Graph: Algorithms and Applications

This article introduces Xiaomi's knowledge graph architecture, presents text‑graph joint representation learning methods, and demonstrates their practical use in entity linking, entity recommendation, and knowledge completion, highlighting model designs, feature engineering, and experimental results.

AIKnowledge Graphentity linking

0 likes · 16 min read

Knowledge Representation Learning for Xiaomi Knowledge Graph: Algorithms and Applications

Sohu Tech Products

Jan 20, 2021 · Artificial Intelligence

Graph Algorithm Design and Optimization for Detecting Black‑Market Users in Virtual Networks

This article presents a comprehensive study on using graph representation learning, particularly GraphSAGE and its optimizations, to identify and mitigate black‑market accounts in virtual networks, covering background, algorithm design, handling isolated nodes and heterogeneity, and evaluation results.

GraphSAGEblack market detectiongraph algorithms

0 likes · 13 min read

Graph Algorithm Design and Optimization for Detecting Black‑Market Users in Virtual Networks

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.

CompletionKnowledge GraphLogical Reasoning

0 likes · 14 min read

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

Didi Tech

May 15, 2020 · Artificial Intelligence

Search Matching Models and Applications in DiDi Food

The article outlines DiDi Food’s search relevance challenge, defines semantic matching versus traditional keyword methods, describes the recall‑ranking pipeline, and reviews three families of deep matching models—representation‑based (e.g., DSSM), interaction‑based (e.g., DRMM) and hybrid (e.g., DUET)—including experimental results and a recruitment notice.

DiDi Fooddeep matchinginformation retrieval

0 likes · 16 min read

Search Matching Models and Applications in DiDi Food

DataFunTalk

Oct 9, 2019 · Artificial Intelligence

Multilingual Content Understanding in UC International Feed Recommendation

This article presents a comprehensive overview of the challenges, requirements, and technical solutions for multilingual content understanding in UC's international information‑flow recommendation system, covering structured signal construction, low‑resource NLP techniques, transfer learning, quality modeling, and image‑based signal integration.

NLPRecommendation Systemscontent understanding

0 likes · 14 min read

Multilingual Content Understanding in UC International Feed Recommendation

DataFunTalk

May 21, 2019 · Artificial Intelligence

Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

This article provides a comprehensive overview of deep learning, covering essential mathematics and machine learning fundamentals, modern deep network architectures and regularization techniques, advanced research topics such as structured probabilistic models and generative methods, and a curated reading list for practitioners.

AI fundamentalsNeural Networksmachine learning

0 likes · 4 min read

Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

Qunar Tech Salon

Apr 26, 2018 · Artificial Intelligence

Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning

The article explains how gcForest, an ensemble‑of‑decision‑tree model that mimics deep neural network hierarchies, uses cascade forests and multi‑grained sliding‑window scanning to achieve effective representation learning with fewer hyper‑parameters, especially on small datasets.

cascade forestensemble methodsgcForest

0 likes · 11 min read

Understanding gcForest: Cascade Forest Structure and Multi‑grained Scanning for Representation Learning

Ctrip Technology

Aug 28, 2017 · Artificial Intelligence

Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases

This article examines the construction, reasoning, and large‑scale applications of knowledge graphs, discussing graph building techniques, storage solutions, deep‑learning‑based entity extraction, inference models such as TransR and RESCAL, and how these graphs enhance search, recommendation, and other AI systems.

Deep LearningKnowledge Graphentity recognition

0 likes · 13 min read

Building and Applying Large‑Scale Knowledge Graphs: Construction, Reasoning, and Use Cases

Ctrip Technology

Jul 29, 2016 · Artificial Intelligence

Reasoning Techniques in Knowledge Graphs and Their Application to a High‑School Exam Robot

The talk reviews the history and concepts of knowledge graphs, explains logical and statistical reasoning methods—including rule‑based and representation‑learning approaches—and demonstrates how these techniques can be applied to build an intelligent robot that assists students in solving high‑school exam problems.

Knowledge Graphexam robotreasoning

0 likes · 5 min read

Reasoning Techniques in Knowledge Graphs and Their Application to a High‑School Exam Robot