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Generative Models

64 articles · Page 1 of 1
Machine Heart
Machine Heart
Aug 9, 2026 · Artificial Intelligence

Let Generative Models Draw Spatial Answers, Not Text Coordinates – Zhejiang’s Agentic Evaluation Framework

The article critiques coordinate‑based spatial benchmarks, introduces the ProVisE framework that lets image‑generation models answer by drawing, describes the automated Agentic Builder for protocol creation, presents the 14‑task SpatialGen‑Bench, and reports that generative models show direct spatial intuition while complementing text‑output VLMs, with detailed failure analysis.

Agentic BuilderGenerative ModelsProVisE
0 likes · 9 min read
Let Generative Models Draw Spatial Answers, Not Text Coordinates – Zhejiang’s Agentic Evaluation Framework
Machine Heart
Machine Heart
Aug 8, 2026 · Artificial Intelligence

Can Generative Models ‘Draw’ Spatial Intelligence? Introducing the Agentic ProVisE Evaluation Framework

The ProVisE framework lets image‑generation models answer spatial questions by drawing directly on the canvas, using visual protocols and parsers, while the Agentic Builder automatically creates these protocols and the SpatialGen‑Bench benchmark reveals complementary strengths between generative models and text‑output VLMs.

Agentic BuilderGenerative ModelsProVisE
0 likes · 9 min read
Can Generative Models ‘Draw’ Spatial Intelligence? Introducing the Agentic ProVisE Evaluation Framework
Kuaishou Tech
Kuaishou Tech
Aug 4, 2026 · Artificial Intelligence

KDD 2026 Highlights: 25 Kuaishou Papers Selected, 3 Oral Presentations

The Kuaishou technology team had 25 papers accepted at the prestigious KDD 2026 conference—including three oral presentations—covering generative recommendation, automated bidding, semantic ID learning, multi‑behavior modeling, and other AI‑driven advances that are already deployed at massive scale on the platform.

AdvertisingGenerative ModelsKDD2026
0 likes · 33 min read
KDD 2026 Highlights: 25 Kuaishou Papers Selected, 3 Oral Presentations
Machine Heart
Machine Heart
Aug 1, 2026 · Artificial Intelligence

Can Generative Models Be Trained End‑to‑End? The Secret Is a Simple For Loop

The article explains why generative models have long suffered from exposure bias and mode blurring, introduces the Explorative Modeling (XM) paradigm that uses a tiny for‑loop to select the best of K candidates during training, and shows how this third scaling axis dramatically improves performance across image, video, and language modalities.

Generative ModelsScalingdeep learning
0 likes · 11 min read
Can Generative Models Be Trained End‑to‑End? The Secret Is a Simple For Loop
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 22, 2026 · Artificial Intelligence

Renowned AI Scholars from SJTU, CUHK (Shenzhen) and Tencent Hunyuan to Present at MLNLP 2026 Symposium

The MLNLP 2026 online symposium on July 26 will feature leading AI researchers from Shanghai Jiao Tong University, CUHK (Shenzhen) and Tencent Hunyuan presenting talks on lifelong learning, generative model fine‑tuning, and unified multimodal reinforcement learning, with registration now open.

AI ConferenceGenerative ModelsLifelong Learning
0 likes · 12 min read
Renowned AI Scholars from SJTU, CUHK (Shenzhen) and Tencent Hunyuan to Present at MLNLP 2026 Symposium
Weekly Large Model Application
Weekly Large Model Application
Jul 22, 2026 · Artificial Intelligence

AI Denoising Shifts from Call Beautification to Voice Intelligence (2025‑2026)

The article explains how AI‑driven noise reduction moves beyond simple call‑beautification to become the essential front‑line for speech recognition, voice agents, and localization, detailing traditional DSP shortcomings, three core AI techniques, latency trade‑offs, and a practical selection guide for various real‑time and offline scenarios.

AI denoisingGenerative Modelsreal-time audio
0 likes · 10 min read
AI Denoising Shifts from Call Beautification to Voice Intelligence (2025‑2026)
Machine Heart
Machine Heart
Jun 26, 2026 · Artificial Intelligence

Un-0: The First Large‑Scale Generative Model Built on Physical Computation, Targeting 1000× AI Energy Savings

Un-0, a generative model that uses a massive network of coupled Kuramoto oscillators as its computational primitive, achieves ImageNet‑64 FID 6.74, demonstrates that physical dynamics can replace traditional neural layers, and proposes a path toward reducing AI inference energy by three orders of magnitude.

Generative ModelsKuramotoUn-0
0 likes · 13 min read
Un-0: The First Large‑Scale Generative Model Built on Physical Computation, Targeting 1000× AI Energy Savings
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jun 22, 2026 · Artificial Intelligence

Highlights from CVPR 2026: Four NIRC Papers on Video Anomaly Detection and Hand Modeling

The author recounts attending CVPR 2026 in Denver, summarizing four NIRC papers—Fine‑VAD, Alert‑CLIP, Clay‑to‑Stone, and a temporal‑content co‑aware diffusion model—while also describing the opening ceremony, poster sessions, workshops, networking with researchers, and memorable moments exploring the city.

CVPR 2026Generative ModelsHand Modeling
0 likes · 8 min read
Highlights from CVPR 2026: Four NIRC Papers on Video Anomaly Detection and Hand Modeling
Alimama Tech
Alimama Tech
May 28, 2026 · Artificial Intelligence

13 KDD'26 Papers from Taobao: Scaling Laws, World Models and New AI Paradigms

The article highlights thirteen Taobao‑group papers accepted at KDD 2026, covering large‑model scaling laws, end‑to‑end generative recommendation, CTR prediction, interactive recommendation agents, LLM‑based pricing, robust auto‑bidding, two‑stage auctions, generative world models, multi‑attribution conversion, uplift modeling and long‑term causal estimation for e‑commerce systems.

CTR predictionGenerative ModelsKDD 2026
0 likes · 29 min read
13 KDD'26 Papers from Taobao: Scaling Laws, World Models and New AI Paradigms
JD Cloud Developers
JD Cloud Developers
Apr 8, 2026 · Artificial Intelligence

How JoyAI-Image-Edit Brings Spatial Intelligence to Open‑Source Image Editing

JoyAI-Image-Edit, an open‑source multimodal foundation model from JD Research Institute, integrates text‑to‑image generation, image understanding, and instruction‑driven spatial editing, achieving world‑leading spatial perception and editing capabilities that unlock new applications across e‑commerce, robotics, 3D reconstruction, and design.

Generative ModelsOpen Source Modelcomputer vision
0 likes · 7 min read
How JoyAI-Image-Edit Brings Spatial Intelligence to Open‑Source Image Editing
DeepHub IMBA
DeepHub IMBA
Mar 1, 2026 · Artificial Intelligence

Demystifying VAE: From Probabilistic Encoding to Latent Space Regularization

This article walks through the fundamentals of variational autoencoders, explaining why they are needed, detailing their three core components, loss formulation, PyTorch implementation, training loop, and multiple inference modes such as anomaly detection, data generation, conditional generation, latent space manipulation, and data imputation.

Conditional VAEGenerative ModelsPyTorch
0 likes · 15 min read
Demystifying VAE: From Probabilistic Encoding to Latent Space Regularization
AI Frontier Lectures
AI Frontier Lectures
Feb 28, 2026 · Artificial Intelligence

Can Reinforcement Learning Revolutionize Text-to-3D Generation? A Deep Dive

This article presents a systematic investigation of applying reinforcement learning to text‑to‑3D generation, detailing reward design, algorithm selection, a new 3D benchmark, a hierarchical GRPO framework, extensive ablations, and the resulting performance gains and limitations.

AI researchGenerative ModelsReward Design
0 likes · 13 min read
Can Reinforcement Learning Revolutionize Text-to-3D Generation? A Deep Dive
DeWu Technology
DeWu Technology
Feb 11, 2026 · Artificial Intelligence

How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations

This article examines the evolution of re‑ranking systems from traditional pointwise models to a two‑stage generation‑evaluation framework, compares autoregressive and non‑autoregressive generative approaches, details inference speed optimizations with GPU and model‑server upgrades, and outlines a future end‑to‑end sequence generation architecture enhanced by reinforcement learning and contrastive learning.

AIGenerative ModelsRecommendation Systems
0 likes · 14 min read
How Generative Models Transform Re‑ranking Architecture for Faster, More Diverse Recommendations
Tencent Advertising Technology
Tencent Advertising Technology
Jan 22, 2026 · Artificial Intelligence

How Tencent’s Bidding Algorithms Evolved from GMPC to GRB: A Deep Dive into Generative RL for Ads

The article reviews the 2025 evolution of Tencent advertising’s bidding system—from the second‑generation GMPC control algorithm through the third‑generation MRB reinforcement‑learning model to the fourth‑generation generative RL GRB—detailing architectural upgrades, multi‑channel modeling, training pipelines, and experimental gains, and outlines the 2026 AI‑agent roadmap.

AdvertisingAlgorithmGenerative Models
0 likes · 15 min read
How Tencent’s Bidding Algorithms Evolved from GMPC to GRB: A Deep Dive into Generative RL for Ads
Kuaishou Tech
Kuaishou Tech
Jan 19, 2026 · Artificial Intelligence

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

OneSug introduces an end‑to‑end generative framework that unifies recall, coarse‑ranking, and fine‑ranking for e‑commerce query suggestion, addressing the limitations of traditional multi‑stage cascades and dramatically improving relevance, efficiency, and business metrics in real‑world deployments.

Generative ModelsRankingRecommendation Systems
0 likes · 10 min read
How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Dec 30, 2025 · Artificial Intelligence

How Dataset Distillation Shrinks Training Data Without Losing Accuracy

This article provides a comprehensive review of dataset distillation, explaining its motivation, core concepts, major algorithmic families, evaluation criteria, and practical applications such as continual learning, federated learning, neural architecture search, and privacy‑preserving AI.

AI efficiencyDataset DistillationDistribution Matching
0 likes · 25 min read
How Dataset Distillation Shrinks Training Data Without Losing Accuracy
Kuaishou Tech
Kuaishou Tech
Nov 20, 2025 · Artificial Intelligence

How UniDex and UniSearch Redefine Video Search with Semantic Indexing and Generative Models

This article explains how Kuaishou’s UniDex replaces traditional term‑based inverted indexes with model‑driven semantic posting lists and how the end‑to‑end UniSearch framework generates video IDs directly from queries, delivering higher relevance, lower latency, and significant online performance gains.

AIGenerative ModelsLarge Language Models
0 likes · 17 min read
How UniDex and UniSearch Redefine Video Search with Semantic Indexing and Generative Models
AI Algorithm Path
AI Algorithm Path
Oct 20, 2025 · Artificial Intelligence

Building a Flow Matching Model from Scratch: Complete Code Walkthrough

This article walks through the full implementation of a flow‑matching generative model in PyTorch, covering dataset creation, a small MLP that learns a time‑dependent velocity field, the flow‑matching loss, training loop, ODE‑based sampling, visualisation of the learned vector field, and a discussion of the method's limitations and possible extensions.

Flow MatchingGenerative ModelsMLP
0 likes · 13 min read
Building a Flow Matching Model from Scratch: Complete Code Walkthrough
HyperAI Super Neural
HyperAI Super Neural
Oct 17, 2025 · Artificial Intelligence

How AI Is Decoding MOFs: From 36 Years of Nobel-Worthy Research to Generative Design

The article traces the 36‑year evolution of metal‑organic frameworks from early coordination polymers to Nobel‑winning breakthroughs, then details how AI‑driven generative models, diffusion techniques, and large language agents are reshaping MOF design, synthesis, and application across energy, environmental, and biomedical fields.

AI-driven Materials DesignGenerative ModelsMOFFlow
0 likes · 15 min read
How AI Is Decoding MOFs: From 36 Years of Nobel-Worthy Research to Generative Design
AI Algorithm Path
AI Algorithm Path
Oct 15, 2025 · Artificial Intelligence

Building a Flow Matching Model from Scratch: Theory Explained

This article walks through the theory behind flow‑matching generative models, contrasting them with diffusion models, detailing the velocity‑field formulation, training objective, and sampling procedure, and includes visual illustrations of the core concepts.

Flow MatchingGenerative ModelsODE
0 likes · 8 min read
Building a Flow Matching Model from Scratch: Theory Explained
DataFunTalk
DataFunTalk
Sep 27, 2025 · Artificial Intelligence

How AI Is Redefining Filmmaking: From Festival Shorts to Feature Films

The article explores how AI models like Seedream and Seedance are reshaping cinema, from AI‑driven short films showcased at the Busan Film Festival to full‑length feature productions, highlighting technical breakthroughs, industry perspectives, and the emerging "AI +" versus "+ AI" production paradigms.

AIAIGCCinema
0 likes · 11 min read
How AI Is Redefining Filmmaking: From Festival Shorts to Feature Films
Data Party THU
Data Party THU
Sep 3, 2025 · Artificial Intelligence

Exploring Multimodal Generative AI: A Tsinghua Tutorial at IJCAI 2025

This article introduces a 1.5‑hour tutorial presented by Tsinghua researchers at IJCAI 2025, covering the latest advances in multimodal generative AI, including multimodal large language models, diffusion models, post‑training generalization techniques, and unified understanding‑generation frameworks.

Generative ModelsIJCAI 2025Large Language Models
0 likes · 5 min read
Exploring Multimodal Generative AI: A Tsinghua Tutorial at IJCAI 2025
JD Tech
JD Tech
May 6, 2025 · Artificial Intelligence

One4All Generative Recommendation Framework for CPS Advertising

This article reviews recent advances in applying large language models to CPS advertising recommendation, outlines business requirements and core technical challenges, proposes an extensible multi‑task generative framework with explicit intent perception and multi‑objective optimization, and presents offline and online performance gains along with future research directions.

CPS advertisingGenerative ModelsLLM
0 likes · 13 min read
One4All Generative Recommendation Framework for CPS Advertising
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 27, 2025 · Artificial Intelligence

SAFE: A Lightweight General AI Image Detection Method Achieving 96.7% Accuracy Across 33 Test Subsets

SAFE is a lightweight AI‑image detection framework using only 1.44 M parameters and 2.30 B FLOPs that preserves fine‑grained artifacts through crop‑based preprocessing, invariant augmentations, and high‑frequency wavelet features, achieving an average 96.7 % accuracy across 33 test subsets and strong generalization to unseen GAN and diffusion generators.

AI image detectionGenerative Modelscomputer vision
0 likes · 11 min read
SAFE: A Lightweight General AI Image Detection Method Achieving 96.7% Accuracy Across 33 Test Subsets
DaTaobao Tech
DaTaobao Tech
Jan 22, 2025 · Artificial Intelligence

AI Trends 2025: Paths to AGI, Scaling Law Evolution, and Industry Impact

The article surveys the AI revolution driven by foundation models and an evolving Scaling Law, outlining four AGI pathways—large models, intelligent robots, brain‑computer interfaces, and digital life—while highlighting transformer‑based convergence, generative‑first‑principle breakthroughs like DeepSeek‑V3, and transformative industry impacts ranging from consumer robots to Medical 2.0, personalized education, and digital‑simulation platforms such as NVIDIA’s Omniverse.

AGIAIAI industry
0 likes · 23 min read
AI Trends 2025: Paths to AGI, Scaling Law Evolution, and Industry Impact
DaTaobao Tech
DaTaobao Tech
Dec 30, 2024 · Artificial Intelligence

AI Research Highlights: AAAI 2025 & NeurIPS 2024 Breakthroughs in Image Generation

This article compiles recent AI research breakthroughs presented at AAAI 2025 and NeurIPS 2024, summarizing eight papers on multi‑condition image generation, mixed auto‑regressive models, hallucination mitigation in vision‑language models, quantized diffusion denoising, facial part swapping, language‑guided concept vectors, attribution consistency, and video virtual try‑on, with links to each work.

AAAI 2025AI researchGenerative Models
0 likes · 13 min read
AI Research Highlights: AAAI 2025 & NeurIPS 2024 Breakthroughs in Image Generation
Alimama Tech
Alimama Tech
Dec 25, 2024 · Artificial Intelligence

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

The paper introduces Contextual Generative Auction (CGA), a generative framework that directly optimizes ad placements while modeling permutation‑level externalities, decouples allocation from payment learning, and achieves near‑optimal Myerson‑style outcomes, delivering up to 3.2% higher RPM, 1.4% more CTR, 6.4% GMV growth, and 3.5% increased advertiser ROI in large‑scale Taobao experiments.

ExternalitiesGenerative ModelsOnline Advertising
0 likes · 18 min read
Contextual Generative Auction with Permutation-level Externalities for Online Advertising
Alimama Tech
Alimama Tech
Dec 17, 2024 · Artificial Intelligence

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

AuctionNet is a newly introduced benchmark that recreates a massive, realistic online advertising auction environment using latent diffusion‑generated traffic data, provides an 80 GB dataset of 5 × 10⁸ logs from 48 bidding agents, and offers baseline evaluations—including an Online LP that outperforms others—supporting thousands of fair NeurIPS 2024 competition submissions and open‑source tools for large‑scale game decision‑making research.

Generative ModelsOnline Advertisingauto-bidding
0 likes · 15 min read
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
Kuaishou Tech
Kuaishou Tech
Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

AdvertisingGenerative ModelsKuaishou
0 likes · 13 min read
NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks
Alimama Tech
Alimama Tech
Dec 4, 2024 · Artificial Intelligence

AIGB: Generative Auto‑Bidding via Diffusion Modeling

AIGB, introduced by Alibaba Mama in 2023, reframes large‑scale ad‑auction auto‑bidding as a generative sequence task using diffusion models, achieving up to 5 % GMV gains, improved stability and interpretability, and is now commercialized, open‑sourced, and featured in a NeurIPS‑endorsed competition.

AIGenerative Modelsauto-bidding
0 likes · 12 min read
AIGB: Generative Auto‑Bidding via Diffusion Modeling
Alimama Tech
Alimama Tech
Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

Generative ModelsMarketing AIOnline Advertising
0 likes · 18 min read
Generative Auto-bidding via Diffusion Modeling (AIGB)
Ops Development & AI Practice
Ops Development & AI Practice
Jul 4, 2024 · Artificial Intelligence

Discriminative vs Generative Models: When to Use Each in AI

The article explains the fundamental differences between discriminative and generative models, detailing their learning objectives, typical algorithms, key characteristics, example implementations, and practical application scenarios, helping readers choose the appropriate model for classification or data‑generation tasks.

AIDiscriminative ModelsGenerative Models
0 likes · 6 min read
Discriminative vs Generative Models: When to Use Each in AI
Meituan Technology Team
Meituan Technology Team
Jul 4, 2024 · Artificial Intelligence

Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches

Meituan’s search advertising has progressed from rule‑based keyword mining to hierarchical recall that partitions traffic and supply, and now to generative recall using large language models, chain‑of‑thought generation, diffusion‑enhanced multimodal vectors, and knowledge distillation, expanding the decision space while tackling compute and ROI challenges.

Generative ModelsLarge Language ModelsMeituan
0 likes · 19 min read
Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches
DataFunSummit
DataFunSummit
May 6, 2024 · Artificial Intelligence

Advances, Model Types, and Open Challenges of AI‑Generated Content (AIGC) with XiaoBu’s Image Generation Progress

This article reviews the definition, key metrics, and major model families of AI‑generated content, details XiaoBu’s recent breakthroughs in image generation, and discusses open research problems such as evaluation gaps, transformer limitations, and the need for richer multimodal intelligence representations.

AIGCGaNGenerative Models
0 likes · 14 min read
Advances, Model Types, and Open Challenges of AI‑Generated Content (AIGC) with XiaoBu’s Image Generation Progress
NewBeeNLP
NewBeeNLP
Mar 28, 2024 · Industry Insights

How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models

Meta introduces a generative recommendation framework (GR) built on the Hierarchical Sequential Transduction Unit (HSTU) that unifies heterogeneous features, treats user behavior as a new modality, and leverages novel encoder and inference optimizations to achieve order‑of‑magnitude scaling in model size, training compute, and online latency while delivering 12‑18% online gains over traditional deep recommendation models.

Generative ModelsHSTUMeta
0 likes · 36 min read
How Meta’s HSTU Architecture Scales Recommendation Systems Beyond Decades of Deep Models
NewBeeNLP
NewBeeNLP
Mar 15, 2024 · Industry Insights

How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering

Meta’s new Generative Recommendation (GR) paper replaces a decade‑old hierarchical feature paradigm with an ultra‑long sequence transformer that directly fuses user profiles, behaviors, and targets, offering stronger feature crossing, richer information utilization, and massive compute gains, while revealing scaling‑law effects in recommendation systems.

Generative ModelsMetaRecommendation Systems
0 likes · 9 min read
How Meta’s Generative Recommendation (GR) Is Redefining Feature Engineering
Model Perspective
Model Perspective
Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

Discriminative ModelsGenerative ModelsSupervised Learning
0 likes · 13 min read
Master the Three Machine Learning Types and Model Paradigms
DaTaobao Tech
DaTaobao Tech
Feb 28, 2024 · Artificial Intelligence

A Survey of Image Quality Evaluation Metrics for Text-to-Image Generation

The survey traces the evolution of image‑quality evaluation for text‑to‑image generation—from early handcrafted edge and color cues, through GAN‑era similarity scores such as IS, FID and KID, to modern perceptual and CLIP‑based metrics like LPIPS, CLIPScore, TRIQ, IQT and human‑preference models—highlighting a shift toward semantic, aesthetic, and text‑image alignment measures and forecasting domain‑specific metrics for future diffusion models.

GaNGenerative Modelsevaluation metrics
0 likes · 18 min read
A Survey of Image Quality Evaluation Metrics for Text-to-Image Generation
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 3, 2023 · Artificial Intelligence

Probability Basics, Discriminative vs Generative Models, and Autoencoders (including Variational Autoencoders)

This article introduces fundamental probability notation, explains the difference between discriminative and generative models, and provides a comprehensive overview of autoencoders and variational autoencoders, covering their architectures, loss functions, latent spaces, and practical applications in image manipulation.

Discriminative ModelsGenerative ModelsVariational Autoencoder
0 likes · 17 min read
Probability Basics, Discriminative vs Generative Models, and Autoencoders (including Variational Autoencoders)
DataFunSummit
DataFunSummit
Nov 16, 2023 · Artificial Intelligence

Application of Language Models in Molecular Structure Prediction

This talk presents how large language models are leveraged for predicting protein, antibody, and RNA structures, covering background, model stability, generative approaches, antibody-specific models, RNA modeling, and protein‑RNA interaction prediction, along with experimental results and future research directions.

AI for BiologyGenerative ModelsRNA modeling
0 likes · 17 min read
Application of Language Models in Molecular Structure Prediction
Kuaishou Tech
Kuaishou Tech
Aug 7, 2023 · Artificial Intelligence

GFN4Rec: Generative Flow Networks for Listwise Recommendation

This paper introduces GFN4Rec, a generative flow network approach for listwise recommendation that models the entire list generation as a probability flow, optimizing list-level reward to simultaneously improve recommendation accuracy and diversity, and validates its effectiveness on multiple datasets and simulators.

AIGFlowNetGenerative Models
0 likes · 8 min read
GFN4Rec: Generative Flow Networks for Listwise Recommendation
AntTech
AntTech
Apr 12, 2023 · Artificial Intelligence

Ant Technology Research Institute Interactive Intelligence Lab – 13 Papers Accepted at CVPR 2023 and Recent AI Research Highlights

The Ant Technology Research Institute’s Interactive Intelligence Lab announced that 13 of its papers were accepted at CVPR 2023, alongside other recent achievements in generative models and 3D vision, highlighting collaborations with top universities and summarizing the lab’s contributions to artificial intelligence research.

3D visionCVPRGenerative Models
0 likes · 6 min read
Ant Technology Research Institute Interactive Intelligence Lab – 13 Papers Accepted at CVPR 2023 and Recent AI Research Highlights
Model Perspective
Model Perspective
Jan 7, 2023 · Artificial Intelligence

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

An extensive overview of supervised learning introduces key concepts, model types, loss functions, optimization methods, linear and generalized linear models, support vector machines, generative approaches, tree and ensemble techniques, as well as foundational learning theory, providing a comprehensive foundation for AI practitioners.

AIGenerative ModelsSVM
0 likes · 9 min read
Mastering Supervised Learning: From Linear Models to SVMs and Beyond
DataFunTalk
DataFunTalk
Dec 30, 2022 · Artificial Intelligence

Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches

This article reviews the challenges of drug package recommendation in smart healthcare and presents two graph‑based solutions—a discriminative model (DPR) that scores existing drug packages and a generative model (DPG) that creates personalized packages—demonstrating superior performance through extensive experiments and analysis.

AI in HealthcareGenerative Modelsdrug recommendation
0 likes · 19 min read
Graph Representation Learning for Drug Package Recommendation: Discriminative and Generative Approaches
Alimama Tech
Alimama Tech
Jul 13, 2022 · Artificial Intelligence

Fully Automatic Template‑Free Image‑Text Creative Generation System

Alibaba Alimama’s fully automatic, template‑free image‑text creative generation system uses deep‑learning models across material mining, layout synthesis, on‑image copy generation, and visual attribute rendering to produce personalized ad creatives directly from product images and metadata, achieving roughly 19 % CTR lift over prior template‑based methods.

AIAd CreativeAutomation
0 likes · 19 min read
Fully Automatic Template‑Free Image‑Text Creative Generation System
Code DAO
Code DAO
May 26, 2022 · Artificial Intelligence

Understanding Denoising Diffusion Probabilistic Models: Fundamentals and Process

This article explains the fundamentals of denoising diffusion probabilistic models, detailing the forward Gaussian noise injection, the reverse reconstruction via learned conditional densities, model architecture, loss functions, and experimental results on synthetic datasets, all supported by key research citations.

Generative ModelsMarkov chainarXiv
0 likes · 8 min read
Understanding Denoising Diffusion Probabilistic Models: Fundamentals and Process
IT Services Circle
IT Services Circle
Apr 13, 2022 · Artificial Intelligence

Introducing DualStyleGAN, RQ‑VAE Transformer, and VFD: Recent CVPR 2022 Open‑Source Algorithms

Jack Cui presents three recently open‑sourced CVPR 2022 algorithms—DualStyleGAN for high‑resolution portrait style transfer, RQ‑VAE Transformer for improved text‑to‑image generation, and VFD for deep‑fake detection—detailing their functionality, usage options, and providing links to code repositories and demo platforms.

AIGenerative ModelsStyle Transfer
0 likes · 5 min read
Introducing DualStyleGAN, RQ‑VAE Transformer, and VFD: Recent CVPR 2022 Open‑Source Algorithms
DataFunSummit
DataFunSummit
Mar 24, 2022 · Artificial Intelligence

An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches

This article provides a comprehensive introduction to Learning to Rank (LTR), describing its four major categories—point‑wise, pair‑wise, list‑wise, and generative models—along with typical algorithms such as Wide & Deep, ESMM, RankNet, LambdaRank, LambdaMART, DLCM, and miRNN, and discusses their architectures, loss functions, and practical considerations in advertising and recommendation systems.

Generative ModelsLearning-to-RankPairwise
0 likes · 22 min read
An Overview of Learning to Rank (LTR) Models: Point‑wise, Pair‑wise, List‑wise, and Generative Approaches
Code DAO
Code DAO
Dec 10, 2021 · Artificial Intelligence

Understanding Variational Autoencoders: From Dimensionality Reduction to Generative Modeling

This article explains the principles of variational autoencoders, starting with dimensionality reduction techniques such as PCA and standard autoencoders, highlighting their limitations for data generation, and then detailing VAE's regularized latent space, variational inference, re‑parameterization, and loss formulation.

Generative ModelsKL DivergenceVAE
0 likes · 18 min read
Understanding Variational Autoencoders: From Dimensionality Reduction to Generative Modeling
JD Tech
JD Tech
Feb 2, 2021 · Artificial Intelligence

Advances and Trends in Multimodal Digital Content Generation and Automatic Text Summarization

The article reviews recent research on multimodal digital content generation and automatic text summarization, outlining the evolution from extractive to abstractive methods, highlighting four key technology trends such as pretrained language models, transformer dominance, knowledge‑enhanced generation, and multimodal‑knowledge joint modeling, and describing an industrial e‑commerce application built on these advances.

Generative ModelsKnowledge IntegrationText Summarization
0 likes · 12 min read
Advances and Trends in Multimodal Digital Content Generation and Automatic Text Summarization
DataFunTalk
DataFunTalk
Dec 14, 2020 · Artificial Intelligence

Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions

This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.

Generative ModelsNLPQuery Expansion
0 likes · 9 min read
Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions
Hulu Beijing
Hulu Beijing
Mar 19, 2019 · Artificial Intelligence

Understanding Variational Autoencoders: Core Concepts and Training Explained

This article introduces Variational Autoencoders (VAEs), compares them with GANs, explains the underlying variational inference principle, and details how VAEs are trained using the evidence lower bound, complemented by visual diagrams and key equations.

Generative ModelsVAEVariational Autoencoder
0 likes · 4 min read
Understanding Variational Autoencoders: Core Concepts and Training Explained
Meituan Technology Team
Meituan Technology Team
Dec 27, 2018 · Artificial Intelligence

AI-Driven Automated Banner Design for Visual Marketing

Meituan’s AI‑driven system automates banner creation by extracting material features, sequencing them with a planner, refining layouts via an optimizer, and rendering images with a generator, while supporting segmentation, template expansion, and multi‑resolution adaptation to reduce designers’ repetitive work and enable mass personalization.

AIGenerative Modelsbanner design
0 likes · 21 min read
AI-Driven Automated Banner Design for Visual Marketing
360 Quality & Efficiency
360 Quality & Efficiency
Oct 26, 2018 · Artificial Intelligence

Machine Learning Methods: Discriminative and Generative Models, Semi‑Supervised Learning, and GAN‑Based Classification

This article explains the distinction between discriminative and generative models, outlines the challenges of limited labeled data, introduces semi‑supervised learning principles, and describes GAN‑based semi‑supervised classification algorithms with illustrative diagrams.

Artificial IntelligenceGaNGenerative Models
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Machine Learning Methods: Discriminative and Generative Models, Semi‑Supervised Learning, and GAN‑Based Classification
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 19, 2018 · Artificial Intelligence

Can Generative Models Boost Visual‑Text Retrieval? Introducing GXN

This paper presents GXN, a generative cross‑modal feature learning framework that enhances image‑text retrieval by incorporating both high‑level semantic similarity and fine‑grained local matching through a three‑step Look‑Imagine‑Match process, achieving state‑of‑the‑art results on MSCOCO and Flickr30K.

Artificial IntelligenceGenerative Modelscross-modal retrieval
0 likes · 6 min read
Can Generative Models Boost Visual‑Text Retrieval? Introducing GXN
Architects Research Society
Architects Research Society
Oct 4, 2015 · Artificial Intelligence

Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data

This NSF‑funded project aims to develop algorithms that incrementally process partially observed data, integrating generative models with reinforcement‑learning policies to decide when to act, applied to simultaneous machine translation and quiz‑bowl style question answering.

Generative Modelsbayesian inferencemachine translation
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Bayesian Thinking on Your Feet: Embedding Generative Models in Reinforcement Learning for Sequentially Revealed Data