representation 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.

Graph Neural NetworksInfoNCELLM

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.

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

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.

GCNGraph Neural Networksadversarial learning

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.

KG reasoningRED-GNNgraph neural network

0 likes · 11 min read

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

DataFunSummit

Jun 26, 2023 · Artificial Intelligence

Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling

This article reviews the fundamentals of graph neural networks and graph representation learning, explains why proteins can be modeled as graphs, and surveys recent GNN‑based applications such as structure prediction, function annotation, protein design, and self‑supervised representation learning, concluding with future research directions.

AlphaFold2Graph Neural NetworksProtein Design

0 likes · 12 min read

Advances in Graph Neural Networks and Graph Representation Learning for Protein Modeling

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 learningrepresentation learning

0 likes · 8 min read

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

DataFunTalk

Jan 20, 2023 · Artificial Intelligence

Practice of Causal Inference Based on Representation Learning: RCT Standards, Joint Tree‑Neural Modeling, RCT‑ODB Fusion, and Feature Decomposition

This article presents a comprehensive industrial‑level guide to causal inference using representation learning, covering proper RCT experiment design, joint modeling of tree and neural networks, fusion of RCT with observational data, and advanced feature‑decomposition techniques to mitigate bias.

Feature DecompositionOnline ExperimentRCT

0 likes · 22 min read

Practice of Causal Inference Based on Representation Learning: RCT Standards, Joint Tree‑Neural Modeling, RCT‑ODB Fusion, and Feature Decomposition

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 applicationsRecommendation systemsgraph neural network

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.

AutoMLGraph Neural Networksembedding

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.

AIRecommendation systemsWorkshop

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.

AutoMLGraph Neural Networksembedding

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 applicationsGraph Neural Networksknowledge graph

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.

Artificial Intelligenceentity linkingentity recommendation

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