transfer learning

AntTech

Mar 4, 2025 · Artificial Intelligence

GraphCLIP and 2D‑TPE: Enhancing Transferability of Graph Models and Table Understanding for Large Language Models

This article introduces GraphCLIP, a self‑supervised graph‑summary pre‑training framework that boosts zero‑ and few‑shot transferability of graph foundation models for text‑attributed graphs, and 2D‑TPE, a two‑dimensional positional encoding method that preserves table structure to markedly improve large language model performance on table‑understanding tasks, while also announcing a live paper session at WWW 2025 featuring the authors.

Graph Neural NetworksPositional EncodingTable Understanding

0 likes · 6 min read

GraphCLIP and 2D‑TPE: Enhancing Transferability of Graph Models and Table Understanding for Large Language Models

DaTaobao Tech

May 17, 2024 · Artificial Intelligence

Understanding Convolutional Neural Networks: Theory, Architecture, and Practical Techniques

The article explains CNN fundamentals—convolution, pooling, and fully‑connected layers—illustrates their implementation for American Sign Language letter recognition, details parameter calculations, demonstrates data augmentation and transfer learning techniques, and highlights how these methods boost image‑classification accuracy to around 92%.

CNNDeep LearningImage Recognition

0 likes · 19 min read

Understanding Convolutional Neural Networks: Theory, Architecture, and Practical Techniques

DataFunSummit

Feb 17, 2024 · Artificial Intelligence

When to Pre‑Train Graph Neural Networks: Data‑Active Pre‑Training and a Graph Generator Framework

This article examines the conditions under which graph neural network pre‑training is beneficial, proposes a data‑centric generator framework to assess transferability, introduces a data‑active pre‑training strategy that selects informative graphs, and presents experimental results showing that using less, well‑chosen data can outperform full‑scale pre‑training.

Graph Neural Networksdata selectiongraph generator

0 likes · 16 min read

When to Pre‑Train Graph Neural Networks: Data‑Active Pre‑Training and a Graph Generator Framework

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.

Graph Neural NetworksNode ClassificationVS-Graph

0 likes · 27 min read

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

DataFunTalk

Oct 15, 2022 · Artificial Intelligence

AutoDL: Automated and Interpretable Deep Learning – Research Highlights from Baidu Big Data Lab

This article reviews Baidu Big Data Lab's recent advances in automated deep learning (AutoDL), covering its research breakthroughs, integration with PaddlePaddle/PaddleHub, industrial deployments, transfer learning innovations, and future directions for AI automation and interpretability.

AI AutomationAutoDLNeural Architecture Search

0 likes · 19 min read

AutoDL: Automated and Interpretable Deep Learning – Research Highlights from Baidu Big Data Lab

DataFunSummit

Sep 8, 2022 · Artificial Intelligence

GAST: Graph Adaptive Semantic Transfer Model for Cross‑Domain Sentiment Analysis

This article introduces GAST, a graph‑adaptive semantic transfer framework that combines POS‑based Transformers and hybrid graph attention to improve cross‑domain sentiment analysis, presents related work, details the model architecture, reports extensive experiments showing state‑of‑the‑art results, and discusses future directions.

GAST modelGraph Neural NetworksNLP

0 likes · 13 min read

GAST: Graph Adaptive Semantic Transfer Model for Cross‑Domain Sentiment Analysis

DataFunSummit

Aug 30, 2022 · Operations

CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms

This article presents the design, implementation, and evaluation of CloudRCA, an intelligent root cause analysis framework for Alibaba Cloud's big‑data computing services, detailing challenges such as heterogeneous data, sample imbalance, and real‑time constraints, and describing the multi‑stage data processing, hierarchical Bayesian modeling, and deployment results that reduce MTTR by 20%.

Big Datacausal inferencecloud computing

0 likes · 16 min read

CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms

DaTaobao Tech

Aug 30, 2022 · Artificial Intelligence

CTNet: Continual Transfer Learning for Cross-Domain Recommendation

CTNet is a continual transfer learning framework that uses a lightweight Adapter to map source‑domain features onto evolving target‑domain recommendation tasks, preserving all model parameters to avoid catastrophic forgetting and delivering substantial gains in click‑through rate, conversion, and overall business performance in Taobao’s cross‑domain e‑commerce scenario.

Adapter ModuleRecommendation systemscontinual learning

0 likes · 12 min read

CTNet: Continual Transfer Learning for Cross-Domain Recommendation

DataFunSummit

Jul 25, 2022 · Artificial Intelligence

Intelligent Creative System at Hello: Business Background, Architecture, Implementation, and Reflections

This article presents Hello's Intelligent Creative project, detailing its business motivations, system architecture, algorithmic choices such as seq2seq, VAE, GAN, and pre‑trained models, the implementation of material libraries, tagging, recall strategies, a creative racing model, performance gains, and future challenges.

Ad GenerationCTR predictionai

0 likes · 16 min read

Intelligent Creative System at Hello: Business Background, Architecture, Implementation, and Reflections

DataFunTalk

Apr 21, 2022 · Artificial Intelligence

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

This article explains why cold‑start is a critical challenge for recommender systems, outlines four practical strategies—generalization, fast data collection, transfer learning, and few‑shot learning—and then details the DropoutNet model, its end‑to‑end training, loss functions, negative‑sampling techniques, and open‑source implementation.

Cold StartDropoutNetembedding

0 likes · 21 min read

Solving Cold‑Start in Recommender Systems: The DropoutNet Approach

Beike Product & Technology

Jan 7, 2022 · Artificial Intelligence

Beike Real Estate NLP Team Wins First Place in CCIR Cup 2021 Intelligent Human‑Computer Interaction Track

The Beike Real Estate NLP team secured first place in the CCIR Cup 2021 Intelligent Human‑Computer Interaction track by applying semi‑supervised and transfer learning techniques to small‑sample intent recognition and slot filling, and also presented the large‑scale Mandarin dialect speech benchmark KeSpeech at NeurIPS 2021.

AI competitionBERTNLP

0 likes · 5 min read

Beike Real Estate NLP Team Wins First Place in CCIR Cup 2021 Intelligent Human‑Computer Interaction Track

DataFunTalk

Dec 24, 2021 · Artificial Intelligence

Large-Scale Pretrained Model Compression and Distillation: AdaBERT, L2A, and Meta‑KD

This article reviews three consecutive works from Alibaba DAMO Academy on compressing and distilling large pretrained language models—AdaBERT, L2A, and Meta‑KD—detailing their motivations, neural‑architecture‑search‑based designs, loss formulations, experimental results, and insights from a Q&A session.

Neural Architecture Searchaiknowledge distillation

0 likes · 10 min read

Large-Scale Pretrained Model Compression and Distillation: AdaBERT, L2A, and Meta‑KD

DataFunSummit

Dec 11, 2021 · Artificial Intelligence

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

This article reviews recent advances in user representation learning for recommender systems, covering self‑supervised pre‑training, lifelong learning, multi‑task modeling, and large‑scale contrastive methods, and provides code and dataset links for key papers such as PeterRec, Conure, DUPN, ShopperBERT, PTUM, UPRec, and LURM.

Recommendation systemspretrainingself-supervised learning

0 likes · 11 min read

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

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.

Deep Learningartificial intelligencedomain adaptation

0 likes · 17 min read

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

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

DataFunTalk

Jul 1, 2021 · Artificial Intelligence

Pre‑Trained Models: Past, Present, and Future – A Comprehensive Survey

This article surveys the evolution of pre‑trained models, covering the origins of transfer and self‑supervised learning, the rise of transformer‑based PTMs such as BERT and GPT, efficient architecture designs, multimodal and multilingual extensions, theoretical analyses, and future research directions for scalable and robust AI systems.

AI researchMultimodalefficient training

0 likes · 27 min read

Pre‑Trained Models: Past, Present, and Future – A Comprehensive Survey

Ctrip Technology

Dec 10, 2020 · Artificial Intelligence

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

This article presents a comprehensive study on automatically extracting theme‑based recommendation reasons for travel content, detailing a three‑stage retrieval framework, the advantages of interactive matching models over classification, rule‑based and back‑translation data augmentation techniques, and various model optimization strategies including priors, transfer learning, seed selection, optimizer choice, and layer‑wise learning rates.

Natural Language ProcessingRecommendation systemsai

0 likes · 19 min read

Automatic Extraction of Theme-based Recommendation Reasons: Framework, Model Selection, Data Augmentation, and Optimization

JD Tech Talk

Oct 12, 2020 · Artificial Intelligence

Transfer Learning for Human Mobility Modeling in New Cities

The paper presented at WWW 2020 proposes a transfer‑learning framework that leverages POI, road‑network and traffic data from existing cities to generate realistic human mobility trajectories for a target city by modeling mobility intentions, origin‑destination pairs, and routes, and validates the approach with extensive experiments across multiple Chinese cities.

aidomain generalizationhuman mobility

0 likes · 10 min read

Transfer Learning for Human Mobility Modeling in New Cities

JD Tech Talk

Sep 30, 2020 · Artificial Intelligence

Secure Training Methods for Federated Transfer Learning

This article reviews the model structure of federated transfer learning and details three secure training approaches—additive homomorphic encryption, ABY, and SPDZ—combined with polynomial approximation, explaining their protocols, steps, and the role of federated transfer learning within the broader federated learning landscape.

Federated LearningSecure Computationhomomorphic encryption

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Secure Training Methods for Federated Transfer Learning

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.

Federated Learningdomain adaptationmodel architecture

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Federated Transfer Learning: Concepts, Examples, and Model Structures