self-supervised learning

DataFunTalk

Jun 12, 2025 · Artificial Intelligence

How Meta’s V‑JEPA 2 Is Pushing AI Toward Human‑Like Physical Understanding

Meta’s newly released V‑JEPA 2 introduces a video‑trained world model that can understand, predict, and plan physical actions, enabling zero‑shot robot control and outperforming existing models on benchmarks like IntPhys 2, MVPBench, and CausalVQA, while outlining future directions for hierarchical and multimodal JEPA architectures.

BenchmarkRoboticsV-JEPA 2

0 likes · 8 min read

How Meta’s V‑JEPA 2 Is Pushing AI Toward Human‑Like Physical Understanding

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

DataFunSummit

Jan 13, 2025 · Artificial Intelligence

Deep Learning Approaches for Solving Graph Optimization Problems

This article reviews the use of deep learning, including supervised, reinforcement, and self‑supervised paradigms, to address graph optimization problems such as facility location and balanced graph partitioning, discusses existing research challenges, presents a three‑stage self‑supervised model with graph contrastive pre‑training, and evaluates its performance on synthetic and real‑world datasets.

Graph Neural Networkscombinatorial optimizationdeep learning

0 likes · 14 min read

Deep Learning Approaches for Solving Graph Optimization Problems

DevOps

Dec 19, 2024 · Artificial Intelligence

Yann LeCun Discusses AI, Self‑Supervised Learning, and the Future of AGI

Yann LeCun, in a half‑hour interview with Indian entrepreneur Nikhil Kamath, explains the fundamentals of artificial intelligence, critiques current transformer models, describes self‑supervised learning, outlines his joint‑embedding predictive architecture, and shares his vision for AGI, open‑source ecosystems, and the role of PhDs for AI entrepreneurs.

AGIArtificial IntelligencePhD

0 likes · 16 min read

Yann LeCun Discusses AI, Self‑Supervised Learning, and the Future of AGI

AntTech

Aug 6, 2024 · Artificial Intelligence

Self‑Supervised Video Copy Localization with Regional Token Representation

The article presents a self‑supervised framework that uses a regional token structure within a Vision Transformer to accurately locate video plagiarism segments, dramatically reducing annotation costs and achieving state‑of‑the‑art performance without manual labeling, while also highlighting its real‑world deployment for copyright protection.

AIcomputer visioncopyright protection

0 likes · 5 min read

Self‑Supervised Video Copy Localization with Regional Token Representation

DataFunSummit

Jul 18, 2024 · Artificial Intelligence

Tencent Music Tianqin Lab’s Practice and Applications of Audio Representation Large Models

This article reviews Tencent Music Tianqin Lab’s research on audio representation large models, covering background, the evolution of audio features, self‑supervised methods such as SimCLR, BYOL, MAE, MLM, benchmark results, multimodal extensions, and real‑world applications like song authenticity detection and search ranking.

Large ModelsTencent Musicaudio representation

0 likes · 20 min read

Tencent Music Tianqin Lab’s Practice and Applications of Audio Representation Large Models

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

Rare Earth Juejin Tech Community

Dec 20, 2023 · Artificial Intelligence

BERT Model Overview: Inputs, Encoder, Fine‑tuning, and Variants

This article explains BERT's WordPiece tokenization, input embeddings (token, segment, and position embeddings), encoder architecture for Base and Large models, fine‑tuning strategies for various NLP tasks, and introduces popular variants such as RoBERTa and ALBERT.

BERTFine-tuningNLP

0 likes · 12 min read

BERT Model Overview: Inputs, Encoder, Fine‑tuning, and Variants

DataFunSummit

Dec 5, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

This article presents a comprehensive study of a scenario‑adaptive and self‑supervised multi‑scenario recommendation model (SASS) for Taobao, detailing its motivation, adaptive multi‑scenario architecture, two‑stage pre‑training and fine‑tuning, experimental validation, deployment in the recall stage, and practical challenges addressed through Q&A.

AlibabaRecommendation systemsmulti-scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS)

AntTech

Nov 7, 2023 · Artificial Intelligence

Multi‑Scale Stochastic Distribution Prediction for User Behavior Representation Learning

The paper proposes a multi‑scale stochastic distribution prediction (MSDP) framework that learns robust user behavior representations by predicting behavior distributions over random time windows, incorporates contrastive regularization, and demonstrates superior performance on both proprietary financial risk data and a public e‑commerce dataset compared with existing masked and next‑behavior pre‑training methods.

AIPretrainingdistribution prediction

0 likes · 13 min read

Multi‑Scale Stochastic Distribution Prediction for User Behavior Representation Learning

Alimama Tech

Sep 12, 2023 · Artificial Intelligence

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

CC‑GNN addresses three drawbacks of existing graph‑neural retrieval for e‑commerce by adding content phrase nodes, scalable meta‑path message passing, and difficulty‑aware noisy contrastive learning with counterfactual augmentation, achieving up to 16 % recall improvement and notably larger gains on long‑tail queries and cold‑start items.

Cold StartGraph Neural Networkscontent collaboration

0 likes · 19 min read

Content Collaborative Graph Neural Network for Large‑Scale E‑commerce Search

DataFunSummit

Jun 23, 2023 · Artificial Intelligence

Frontiers of Video Action Recognition: Concepts, Algorithms, and Applications

This article introduces video action recognition, covering its basic definition, downstream tasks, major algorithmic families—including CNN‑based, Vision‑Transformer, self‑supervised, and multimodal approaches—and discusses practical deployment scenarios and open challenges in the field.

CNNMultimodal ModelsVision Transformer

0 likes · 16 min read

Frontiers of Video Action Recognition: Concepts, Algorithms, and Applications

DataFunTalk

Apr 10, 2023 · Artificial Intelligence

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

This article presents a comprehensive study of multi‑scenario personalized recommendation, introducing a scenario‑adaptive and self‑supervised model (SASS) that jointly addresses data sparsity, domain adaptation, and recall‑stage deployment through a two‑stage training pipeline and extensive experiments on Alibaba’s Taobao platform.

AlibabaRecommendation systemsmulti-scenario modeling

0 likes · 36 min read

Scenario-Adaptive and Self-Supervised Multi-Scenario Personalized Recommendation (SASS): Design, Training, and Deployment

DataFunTalk

Mar 16, 2023 · Artificial Intelligence

Review of Deep Learning Model Evolution and Future Trends

The article reviews the past six years of deep learning model development, highlighting scaling limits, universality of Transformers, challenges in interpretability and control, and predicts future trends such as efficient architectures, multimodal capabilities, reinforcement learning in virtual worlds, and novel AI hardware, while also promoting a new deep‑learning practice ebook.

AI TrendsAI hardwareModel Scaling

0 likes · 6 min read

Review of Deep Learning Model Evolution and Future Trends

DataFunTalk

Feb 25, 2023 · Artificial Intelligence

Review of Deep Learning Model Evolution and Future Trends

The article reviews the historical development of deep learning models, highlights current limitations such as scaling inefficiencies, interpretability, and planning, and outlines future directions including efficient architectures, self‑supervised training, cross‑modal transformers, and the impact of AI on fields like life sciences and finance.

AI TrendsModel ScalingTransformer

0 likes · 6 min read

DataFunTalk

Feb 25, 2023 · Artificial Intelligence

The Evolution of Modern AI: From Deep Learning Foundations to ChatGPT and Future Directions

This article traces the development of artificial intelligence from its early conceptual roots and the 2012 deep‑learning breakthrough through the rise of self‑supervised large language models like BERT and GPT, explains ChatGPT’s architecture and RLHF training, and discusses its commercial impact and future prospects for fields such as life sciences.

AI applicationsChatGPTRLHF

0 likes · 19 min read

The Evolution of Modern AI: From Deep Learning Foundations to ChatGPT and Future Directions

DataFunTalk

Feb 20, 2023 · Artificial Intelligence

Review of Deep Learning Model Evolution and Future Trends

The article reviews the historical development of deep learning models, highlighting patterns such as scaling limits, increasing generality, interpretability challenges, planning deficiencies, and hardware constraints, and then outlines future directions including efficient architectures, enhanced capabilities, interdisciplinary applications, virtual agents, and novel AI hardware.

AI TrendsModel ScalingTransformer

0 likes · 6 min read

DataFunTalk

Feb 15, 2023 · Artificial Intelligence

Three Emerging Directions for Next‑Generation Large Language Models

The article outlines three promising research avenues—self‑generated training data, model‑driven fact‑checking, and sparse expert architectures—that could shape the next wave of large language model innovation and address current limitations such as data scarcity and hallucinations.

AI researchlarge language modelsmodel self‑improvement

0 likes · 14 min read

Three Emerging Directions for Next‑Generation Large Language Models

DaTaobao Tech

Dec 28, 2022 · Artificial Intelligence

Adaptive Multi-Scenario Modeling for Taobao Personalized Recommendation

On January 9 at 7 p.m., Alibaba senior algorithm engineer Zhang Yuanliang will present a scenario‑adaptive, self‑supervised model for multi‑scenario personalized recommendation, discussing its background, technical details, experimental results, and real‑world deployment within Taobao’s recommendation system.

AIAlibabamulti-scenario modeling

0 likes · 1 min read

Adaptive Multi-Scenario Modeling for Taobao Personalized Recommendation

DataFunSummit

Dec 23, 2022 · Artificial Intelligence

Data‑Centric AI Practices for Content Moderation at NetEase Yidun

The article presents NetEase Yidun’s data‑centric AI approach to content moderation, covering the background of Data‑Centric AI, the specific business and data challenges of content safety, comprehensive data pipelines—including collection, labeling, augmentation, selection, cleaning, iteration and testing—and the role of self‑, semi‑ and weak‑supervised learning in enhancing algorithm performance.

Algorithm InnovationData-Centric AISemi-supervised Learning

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Data‑Centric AI Practices for Content Moderation at NetEase Yidun