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DataFunSummit
DataFunSummit
Oct 7, 2025 · Artificial Intelligence

Deep Thinking in Large Language Models: Overcoming Domain Challenges

This presentation explores how large language models can transcend their general knowledge limits by developing domain‑specific deep thinking abilities, addressing challenges such as complex instruction execution, expert reasoning gaps, and tool integration, and proposes reinforcement‑learning‑driven frameworks, structured thinking pipelines, and tool‑calling mechanisms to achieve rational intelligence.

Tool integrationdeep reasoningdomain adaptation
0 likes · 27 min read
Deep Thinking in Large Language Models: Overcoming Domain Challenges
HyperAI Super Neural
HyperAI Super Neural
Sep 28, 2025 · Artificial Intelligence

Weekly AI Paper Digest: Vision‑Language Models for Safety, Unstable Singularities, and RL‑Driven Reasoning

This week’s AI paper roundup highlights five recent studies: a construction‑site vision‑language dataset and safety inspection tasks, a deep CORAL method for unsupervised domain adaptation, the discovery of a new family of unstable singularities in nonlinear PDEs, a reinforcement‑learning approach that boosts reasoning in large language models, and the PANORAMA architecture for omnidirectional vision in embodied AI.

Construction SafetyOmnidirectional VisionPDE Research
0 likes · 6 min read
Weekly AI Paper Digest: Vision‑Language Models for Safety, Unstable Singularities, and RL‑Driven Reasoning
AIWalker
AIWalker
Sep 17, 2025 · Artificial Intelligence

Cutting-Edge Attention Mechanism Innovations for 2025: Modal Fusion and Domain Adaptation

This article surveys 183 recent attention‑mechanism papers, classifies them into four innovation categories, and highlights representative works such as MILA, ARFFT, CNN‑Transformer for speech emotion, and LSTM‑attention epidemic forecasting, providing concrete methods, code links, and performance insights.

2025Attention MechanismDeep Learning
0 likes · 7 min read
Cutting-Edge Attention Mechanism Innovations for 2025: Modal Fusion and Domain Adaptation
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 25, 2024 · Artificial Intelligence

How to Use Importance Sampling for Effective Continue Pretraining of LLMs

Continuing pretraining (CP) bridges pretraining and SFT to inject domain knowledge, but faces catastrophic forgetting; this article explores leveraging importance sampling to balance common and domain data, discusses data selection, annealing strategies, and practical tips for mitigating forgetting while enhancing specialized capabilities.

Catastrophic ForgettingContinue PretrainingImportance Sampling
0 likes · 8 min read
How to Use Importance Sampling for Effective Continue Pretraining of LLMs
DataFunSummit
DataFunSummit
Sep 13, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Workshop Lab

This article presents the Fudan University Knowledge Workshop Lab's comprehensive research on domain large models, covering background, domain adaptation, capability enhancement, collaborative workflows, challenges such as inference cost and alignment, and proposed solutions including source‑enhanced training, self‑correction mechanisms, and hybrid retrieval‑augmented generation.

AI researchKnowledge Graphsdomain adaptation
0 likes · 16 min read
Research on Domain Large Models by Fudan University Knowledge Workshop Lab
Tencent Advertising Technology
Tencent Advertising Technology
Aug 26, 2024 · Artificial Intelligence

ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising

ADSNet introduces an adaptive Siamese network for cross‑domain lifetime value (LTV) prediction in advertising, leveraging external channel data to mitigate sample sparsity, employing gain‑based sample selection, domain adaptation, and ordinal classification to improve pLTV accuracy and address negative transfer.

AdvertisingLTV predictionadaptive siamese network
0 likes · 12 min read
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising
DataFunTalk
DataFunTalk
Jul 9, 2024 · Artificial Intelligence

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

This article presents an overview of graph knowledge transfer, discussing the data‑hungry problem, distribution shift in graph data, the Knowledge Bridge Learning (KBL) paradigm, the Bridged‑GNN implementation, experimental results across multiple scenarios, and future research directions.

Knowledge Transferbridged-GNNdomain adaptation
0 likes · 19 min read
Graph Knowledge Transfer and the Knowledge Bridge Learning Framework
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 27, 2024 · Artificial Intelligence

Engineering Data for R&D Large Language Models: From Pre‑training to Prompt Design

This article presents a comprehensive guide to data engineering for research‑focused large language models, covering domain‑adaptive pre‑training, supervised fine‑tuning, retrieval‑augmented generation, dataset construction, data cleaning pipelines, token‑izer adaptation, and prompt engineering best practices to boost model performance in specialized tasks.

Fine‑TuningLLMRAG
0 likes · 20 min read
Engineering Data for R&D Large Language Models: From Pre‑training to Prompt Design
NewBeeNLP
NewBeeNLP
Jun 24, 2024 · Artificial Intelligence

How Domain Large Models Are Shaping the Future of AI: Challenges and Solutions

This article reviews Fudan University's Knowledge Factory Lab research on domain large models, covering background, three major deployment challenges, data‑selection strategies, ability‑enhancement techniques, collaborative workflows, and retrieval‑augmented generation methods that aim to make large models practical for real‑world tasks.

Model Alignmentdomain adaptationknowledge extraction
0 likes · 18 min read
How Domain Large Models Are Shaping the Future of AI: Challenges and Solutions
DataFunTalk
DataFunTalk
Jun 15, 2024 · Artificial Intelligence

Research on Domain Large Models by Fudan University Knowledge Factory Lab

This article presents Fudan University's Knowledge Factory Lab research on domain large models, covering background, challenges, data selection, source‑enhanced tagging, capability improvements, self‑correction, collaborative workflows, and retrieval‑augmented generation for practical AI deployment.

AI researchKnowledge Graphdomain adaptation
0 likes · 16 min read
Research on Domain Large Models by Fudan University Knowledge Factory Lab
DataFunSummit
DataFunSummit
Apr 28, 2024 · Artificial Intelligence

Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework

This article presents a comprehensive overview of graph knowledge transfer, covering its definition, the data‑hungry problem, distribution shift challenges, the Knowledge Bridge Learning (KBL) framework, the Bridged‑GNN model, extensive experiments on real‑world scenarios, and a concluding Q&A session.

Knowledge Transferdomain adaptationgraph learning
0 likes · 22 min read
Graph Knowledge Transfer: Methods, Practices, and the Knowledge Bridge Learning Framework
DataFunTalk
DataFunTalk
Feb 24, 2024 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.

Deep Learningcausal discoverycausal inference
0 likes · 11 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
DataFunTalk
DataFunTalk
Jan 16, 2024 · Artificial Intelligence

Applying Knowledge Graphs to E‑commerce AIGC: From Domain‑Specific to General Knowledge Graphs and LLM Integration

This article presents a comprehensive overview of how knowledge graphs are leveraged in e‑commerce AIGC pipelines, detailing domain‑specific and general graph‑based text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language model integration.

AIGCKnowledge GraphText Generation
0 likes · 22 min read
Applying Knowledge Graphs to E‑commerce AIGC: From Domain‑Specific to General Knowledge Graphs and LLM Integration
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 11, 2023 · Artificial Intelligence

How Hyperbolic Space and Contrastive Learning Boost Domain-Specific Language Models

This article introduces the KANGAROO model, which injects hierarchical semantic information via hyperbolic space and leverages contrastive learning on dense subgraph structures to overcome global sparsity in vertical‑domain knowledge‑enhanced pre‑trained language models, and evaluates its performance on finance and medical tasks.

NLPcontrastive learningdomain adaptation
0 likes · 10 min read
How Hyperbolic Space and Contrastive Learning Boost Domain-Specific Language Models
DataFunSummit
DataFunSummit
Dec 9, 2023 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.

Deep Learningcausal discoverycausal inference
0 likes · 12 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
Meituan Technology Team
Meituan Technology Team
Jun 15, 2023 · Artificial Intelligence

Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights

This article reviews eight CVPR 2023 papers selected by Meituan’s technology team, covering self‑supervised learning, domain adaptation, federated learning, object detection, 3D reconstruction, GAN‑based pre‑training, RGB‑T tracking, vision‑language navigation, and visual‑textual layout generation, highlighting each work’s methodology, experiments, and reported performance gains.

3D Object DetectionCVPR 2023Computer Vision
0 likes · 15 min read
Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights
DataFunTalk
DataFunTalk
Jun 4, 2023 · Artificial Intelligence

Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems

This presentation introduces a decoupled domain‑adaptation network that separates popularity and attribute representations to mitigate popularity bias in recommender systems, describing the problem, existing IPS and causal‑inference solutions, the CD2AN architecture, experimental results, and practical Q&A.

AIdomain adaptationmachine learning
0 likes · 13 min read
Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems
DataFunTalk
DataFunTalk
May 24, 2023 · Artificial Intelligence

Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks

This article reviews recent advances in graph transfer learning, introduces the novel VS-Graph scenario for knowledge transfer between dominant and silent nodes, and details the Knowledge Transferable Graph Neural Network (KTGNN) framework with domain‑adaptive feature completion, message passing, and transferable classifier modules, highlighting experimental results and future research directions.

AIKnowledge TransferVS-Graph
0 likes · 27 min read
Graph Transfer Learning and VS-Graph: Knowledge Transferable Graph Neural Networks
DataFunTalk
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
Alimama Tech
Alimama Tech
Dec 7, 2022 · Artificial Intelligence

Adaptive Domain Interest Network for Multi-domain Recommendation

The Adaptive Domain Interest Network (ADIN) introduces a shared backbone with scenario‑specific subnetworks, domain‑specific batch normalization and SE‑Block attention to capture both commonalities and divergences across recommendation scenarios, and, combined with self‑supervised training, consistently outperforms baselines, delivering a 1.8% revenue lift in Alibaba’s display‑ad platform and now runs in production.

Deep Learningdomain adaptationrecommendation
0 likes · 12 min read
Adaptive Domain Interest Network for Multi-domain Recommendation
DataFunTalk
DataFunTalk
Nov 23, 2022 · Artificial Intelligence

Lightweight Adaptation Techniques for Multimodal Large Models

This article presents a comprehensive overview of lightweight adaptation methods—including language, domain, and optimization‑goal adapters and structured prompts—to overcome language mismatch, low domain fit, and objective differences when deploying open‑source multimodal large models in real‑world AI applications.

AIAdapterModel Adaptation
0 likes · 14 min read
Lightweight Adaptation Techniques for Multimodal Large Models
DaTaobao Tech
DaTaobao Tech
May 31, 2022 · Artificial Intelligence

Decoupling Popularity Bias in Dual‑Tower Retrieval Models

The paper proposes CDAN, a dual‑tower retrieval model that separates item attribute and popularity representations via a Feature Decoupling Module with orthogonal embeddings, aligns head‑tail attribute distributions using MMD and contrastive learning, and jointly trains biased and unbiased towers, achieving higher tail recall, lower exposure concentration, and measurable online click‑through improvements.

Recommendation Systemscontrastive learningdomain adaptation
0 likes · 13 min read
Decoupling Popularity Bias in Dual‑Tower Retrieval Models
Alimama Tech
Alimama Tech
May 25, 2022 · Artificial Intelligence

UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation

The paper introduces UKD, an uncertainty‑regularized knowledge‑distillation framework that uses a click‑adaptive teacher to generate pseudo‑conversion labels for unclicked impressions and trains a student model with uncertainty‑weighted loss, thereby mitigating sample‑selection bias and achieving up to 3.4% CVR improvement and 4.3% CPA reduction on large‑scale advertising datasets.

CVR debiasingadvertising algorithmsconversion rate estimation
0 likes · 20 min read
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Code DAO
Code DAO
Apr 24, 2022 · Artificial Intelligence

How Transfer Learning Accelerates Deep Learning Across Vision, NLP, and Reinforcement Learning

The article explains how transfer learning reduces data and time requirements in deep learning by reusing pretrained models for vision, natural language processing, and reinforcement learning, while discussing challenges such as overfitting, the need for progressive networks, entropy regularization, domain adaptation, multi‑task learning, and model distillation.

Deep Learningdomain adaptationmodel distillation
0 likes · 10 min read
How Transfer Learning Accelerates Deep Learning Across Vision, NLP, and Reinforcement Learning
DataFunSummit
DataFunSummit
Mar 28, 2022 · Artificial Intelligence

Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions

This talk presents a comprehensive overview of music-domain Named Entity Recognition, covering its definition, unique challenges, candidate generation, training data construction, offline and online system architecture, successive model improvements (V1‑V3), knowledge‑fusion techniques, and future research directions.

MusicNERdomain adaptation
0 likes · 25 min read
Music Domain Named Entity Recognition: Challenges, Solutions, and Future Directions
DataFunSummit
DataFunSummit
Sep 30, 2021 · Artificial Intelligence

Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Results

This article introduces the fundamentals of transfer learning, formalizes its theoretical foundations, and demonstrates how multi‑task learning and domain adaptation techniques can be applied to financial risk control to overcome label scarcity, distribution shift, and improve model performance.

artificial intelligencedomain adaptationfinancial risk control
0 likes · 17 min read
Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Results
Youku Technology
Youku Technology
Sep 29, 2021 · Artificial Intelligence

Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment

By constructing virtual mirror samples that occupy identical positions across source and target domains, the authors eliminate covariate shift while preserving distribution structure, enabling superior unsupervised domain adaptation that achieves state‑of‑the‑art performance on Office and VisDA benchmarks and improves real‑world lighting and gender‑recognition tasks.

AI researchSOTAcovariate shift
0 likes · 3 min read
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
DataFunTalk
DataFunTalk
Sep 27, 2021 · Artificial Intelligence

Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation

This article introduces the fundamentals of transfer learning, explains its theoretical foundations and formulas, and demonstrates how multi‑task learning and domain‑adaptation techniques are applied to financial risk‑control scenarios to overcome label scarcity, distribution shift, and model complexity challenges, presenting detailed experimental results and analysis.

Deep LearningModel Evaluationdomain adaptation
0 likes · 17 min read
Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation
Kuaishou Tech
Kuaishou Tech
Jun 21, 2021 · Artificial Intelligence

Kuaishou’s CVPR 2021 Paper Highlights: 3D Vision, Domain Adaptation, Point Cloud Completion, Video Segmentation, and Face Forgery Detection

Kuaishou secured 14 accepted papers at CVPR 2021, spanning 3D hand mesh recovery, unsupervised keypoint detection, point cloud completion, modular interactive video segmentation, deep video matting, co‑salient object detection, occlusion‑aware instance segmentation, semantic image matting, and face forgery detection, showcasing the maturity of its research collaborations.

CVPRFace Forgery Detectiondomain adaptation
0 likes · 14 min read
Kuaishou’s CVPR 2021 Paper Highlights: 3D Vision, Domain Adaptation, Point Cloud Completion, Video Segmentation, and Face Forgery Detection
Kuaishou Large Model
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
Meituan Technology Team
Meituan Technology Team
Apr 15, 2021 · Artificial Intelligence

Meituan Technical Team Shares CVPR 2021 Pre-lecture: Five Papers on Video Instance Segmentation, Facial Expression Recognition, Real-time Semantic Segmentation, Weakly Supervised Semantic Segmentation, and Multi-source Domain Adaptation

At a CVPR 2021 pre‑lecture, Meituan’s Visual Intelligence Center showcased five cutting‑edge papers—VisTR transformer‑based video instance segmentation, a feature‑decomposition facial expression recognizer, an accelerated BiSeNet for real‑time semantic segmentation, an embedded discriminative attention mechanism for weakly supervised segmentation, and a partial‑feature selection framework for multi‑source domain adaptation—highlighting the company’s large AI R&D team, university collaborations, real‑world deployment across its services, and ongoing recruitment.

AICVPR2021Facial Expression Recognition
0 likes · 10 min read
Meituan Technical Team Shares CVPR 2021 Pre-lecture: Five Papers on Video Instance Segmentation, Facial Expression Recognition, Real-time Semantic Segmentation, Weakly Supervised Semantic Segmentation, and Multi-source Domain Adaptation
Youku Technology
Youku Technology
Dec 9, 2020 · Artificial Intelligence

Four Alibaba Papers Accepted at AAAI 2021: Bandits, Video Adaptation, Sentiment, Segmentation

AAAI 2021, the premier AI conference with a 21.4% acceptance rate, accepted four papers from Alibaba Entertainment covering non‑stationary stochastic bandits with graph feedback, spatial‑temporal causal inference for image‑to‑video adaptation, a unified MRC framework for aspect‑based sentiment analysis, and amodal segmentation using shape priors.

AAAI 2021Alibaba ResearchAmodal Segmentation
0 likes · 5 min read
Four Alibaba Papers Accepted at AAAI 2021: Bandits, Video Adaptation, Sentiment, Segmentation
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 18, 2020 · Artificial Intelligence

iCartoonFace: A Large-Scale Cartoon Face Recognition Dataset and Multi‑Task Learning Framework

The paper presents iCartoonFace, the largest manually annotated cartoon‑face dataset with 5,013 identities and 389,678 images, and a multi‑task learning framework that jointly trains on cartoon and real faces using classification, unknown‑identity rejection, and domain‑adaptation losses, achieving state‑of‑the‑art recognition despite pose, occlusion, and illumination challenges.

domain adaptationface recognitionmulti-task learning
0 likes · 10 min read
iCartoonFace: A Large-Scale Cartoon Face Recognition Dataset and Multi‑Task Learning Framework
JD Tech Talk
JD Tech Talk
Sep 17, 2020 · Artificial Intelligence

Federated Transfer Learning: Concepts, Examples, and Model Structures

This article introduces the fundamentals of transfer learning and federated transfer learning, explains domain adaptation for sentiment analysis, presents two illustrative examples—mid-level image feature transfer and text-to-image transfer—and outlines the model architecture and loss functions of federated transfer learning frameworks.

Model architectureSentiment Analysisdomain adaptation
0 likes · 14 min read
Federated Transfer Learning: Concepts, Examples, and Model Structures
DataFunTalk
DataFunTalk
May 29, 2020 · Artificial Intelligence

Model‑Independent Learning: Multi‑Task Learning and Transfer Learning

This article explains two model‑independent learning paradigms—multi‑task learning and transfer learning—detailing their motivations, sharing mechanisms, training procedures, theoretical formulations, and practical benefits such as improved generalization, data efficiency, and domain‑invariant representations.

Deep Learningdomain adaptationmachine learning
0 likes · 21 min read
Model‑Independent Learning: Multi‑Task Learning and Transfer Learning
DataFunTalk
DataFunTalk
Nov 15, 2019 · Artificial Intelligence

MT-BERT: Domain‑Adapted BERT Pre‑training and Fine‑tuning for Meituan‑Dianping NLP Tasks

This article describes the development of MT‑BERT, a BERT‑based language model pre‑trained on Meituan‑Dianping business data, its distributed mixed‑precision training pipeline, domain adaptation, knowledge‑graph integration, model compression techniques, and the wide range of downstream NLP applications achieved in the platform.

BERTKnowledge GraphMeituan
0 likes · 31 min read
MT-BERT: Domain‑Adapted BERT Pre‑training and Fine‑tuning for Meituan‑Dianping NLP Tasks
Didi Tech
Didi Tech
Jul 13, 2019 · Artificial Intelligence

Computer Vision in Transportation Workshop – Course Overview and Highlights

The Didi Computer Vision in Transportation workshop teaches fundamentals, advanced domain‑adaptation and lightweight model techniques, and real‑world applications such as driver identification and driving‑scenario analysis, delivered by Didi AI Labs experts, emphasizing practical use cases and cutting‑edge research.

AIDriver IdentificationLightweight Models
0 likes · 6 min read
Computer Vision in Transportation Workshop – Course Overview and Highlights
AntTech
AntTech
May 25, 2018 · Artificial Intelligence

Insights from AAAI 2018: Conference Overview, Paper Highlights, and Ant Financial Contributions

The article provides a comprehensive overview of the AAAI 2018 conference, including submission statistics, country rankings, popular research tracks, award-winning papers, detailed summaries of notable AI papers such as GraphGAN, HARP, PrivSR, and domain adaptation, as well as Ant Financial's own contributions like cw2vec and privacy‑preserving recommendation systems.

AAAI 2018Privacy-Preserving Recommendationartificial intelligence
0 likes · 15 min read
Insights from AAAI 2018: Conference Overview, Paper Highlights, and Ant Financial Contributions