domain adaptation

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 GraphsRetrieval-Augmented Generation

0 likes · 16 min read

Research on Domain Large Models by Fudan University Knowledge Workshop Lab

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.

LTV predictionadaptive siamese networkadvertising

0 likes · 12 min read

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

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.

Graph Neural Networksbridged-GNNdomain adaptation

0 likes · 19 min read

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

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 researchRetrieval-Augmented Generationdomain adaptation

0 likes · 16 min read

Research on Domain Large Models by Fudan University Knowledge Factory Lab

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.

Graph Neural Networksdomain adaptationgraph learning

0 likes · 22 min read

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

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.

causal discoverycausal inferencecausal learning

0 likes · 11 min read

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

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.

AIGCdomain adaptatione-commerce

0 likes · 22 min read

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

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.

causal discoverycausal inferencecausal learning

0 likes · 12 min read

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

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.

AIGraph Neural NetworksNode Classification

0 likes · 27 min read

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

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

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.

advertisingdeep learningdomain adaptation

0 likes · 12 min read

Adaptive Domain Interest Network for Multi-domain Recommendation

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.

AIMultimodal Modelsadapter

0 likes · 14 min read

Lightweight Adaptation Techniques for Multimodal Large Models

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

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

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.

Artificial IntelligenceKnowledge FusionMusic

0 likes · 25 min read

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

DataFunSummit

Jan 16, 2022 · Artificial Intelligence

Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation

This talk presents an overview of text‑plus‑speech multimodal emotion analysis, covering background, single‑modal text and audio models, the MSCNN‑SPU multimodal architecture, domain‑adaptation techniques, and future directions, with detailed model explanations, experimental results, and practical deployment insights.

Audio ProcessingSpeech RecognitionText Classification

0 likes · 40 min read

Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation

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 Intelligencedeep learningdomain adaptation

0 likes · 17 min read

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

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

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 learningdomain adaptationfinancial risk

0 likes · 17 min read

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