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1241 articles

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Jul 9, 2022 · Artificial Intelligence

User Behavior Sequence Based Transaction Anti‑Fraud Detection

This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.

Deep LearningEmbeddingfraud detection

0 likes · 12 min read

User Behavior Sequence Based Transaction Anti‑Fraud Detection

Meituan Technology Team

Jul 6, 2022 · Artificial Intelligence

Engineering Practices for Large-Scale Deep Learning Models in Meituan Takeaway Advertising

The article details Meituan's engineering journey from small DNNs to hundred‑gigabyte deep learning models for food‑delivery ads, analyzing online latency and offline efficiency challenges and presenting distributed storage, CPU/GPU acceleration, OpenVINO, TensorRT, CodeGen, and data‑pipeline optimizations that dramatically improve throughput, memory usage, and sample‑building speed.

CPU accelerationDeep LearningFeature Engineering

0 likes · 45 min read

Engineering Practices for Large-Scale Deep Learning Models in Meituan Takeaway Advertising

Alimama Tech

Jul 6, 2022 · Artificial Intelligence

How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval

This article presents AMCAD, an adaptive mixed‑curvature graph embedding system that models heterogeneous e‑commerce search ad graphs in non‑Euclidean spaces, detailing its sample construction, three‑stage model architecture, offline and online experiments, and demonstrating significant performance gains over Euclidean baselines.

Deep Learningadvertisement retrievale‑commerce

0 likes · 13 min read

How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval

Bilibili Tech

Jul 1, 2022 · Artificial Intelligence

Quality‑Controlled Scene‑Adaptive Video Transcoding System at Bilibili

Bilibili’s quality‑controlled scene‑adaptive transcoding system automatically splits videos into shot‑level segments, predicts optimal encoding parameters with a deep‑learning model, applies two‑pass VMAF‑targeted encoding and ROI‑aware bitrate allocation, achieving stable visual quality, 99% accuracy, and roughly 15% bitrate reduction.

Deep LearningROI encodingVMAF

0 likes · 25 min read

Quality‑Controlled Scene‑Adaptive Video Transcoding System at Bilibili

360 Quality & Efficiency

Jul 1, 2022 · Artificial Intelligence

Building an End-to-End Image Search System with Milvus and VGG

This article presents a complete image‑search solution that extracts visual features with the VGG16 model, stores them in the Milvus vector database, and provides a set of web APIs for training, querying, counting, searching, and deleting image vectors, all deployed via Docker containers.

AIDeep LearningMilvus

0 likes · 7 min read

Building an End-to-End Image Search System with Milvus and VGG

AntTech

Jun 24, 2022 · Artificial Intelligence

Hierarchical Residual Network for Multi‑Granularity Classification (HRN) – CVPR 2022 Paper Overview

This article presents a CVPR 2022 paper by Zhejiang University and Ant Group that introduces a label‑relation‑tree‑based Hierarchical Residual Network (HRN) for improving multi‑granularity image classification, detailing its motivation, architecture, composite loss design, extensive experiments on fine‑grained datasets, and practical impact on content‑security applications.

CVPR2022Deep LearningHierarchical Classification

0 likes · 12 min read

Hierarchical Residual Network for Multi‑Granularity Classification (HRN) – CVPR 2022 Paper Overview

DataFunSummit

Jun 21, 2022 · Artificial Intelligence

JiuGe: An Automatic Chinese Classical Poetry Generation System – Algorithms and Research Overview

This article presents the JiuGe system developed by THUNLP for automatically generating Chinese classical poetry, detailing its research motivations, model architecture—including salient‑clue, working‑memory, topic‑memory, style‑transfer and reinforcement‑learning components—implementation, applications, and future directions.

Artificial IntelligenceDeep LearningKnowledge Graph

0 likes · 18 min read

JiuGe: An Automatic Chinese Classical Poetry Generation System – Algorithms and Research Overview

DataFunTalk

Jun 15, 2022 · Artificial Intelligence

Data Interaction Based Click‑Through Rate Model (RIM): Review, Architecture, and Experimental Insights

This article reviews the evolution of click‑through rate (CTR) prediction models from early logistic regression and factorization machines to deep neural networks, introduces the data‑interaction based RIM (Retrieval & Interaction Machine) architecture with its search and prediction modules, and presents extensive experimental comparisons and future research directions.

CTRDeep LearningRIM

0 likes · 14 min read

Data Interaction Based Click‑Through Rate Model (RIM): Review, Architecture, and Experimental Insights

DataFunSummit

Jun 14, 2022 · Artificial Intelligence

Practical Acceleration of Deep Model Inference: Case Studies and Optimization Techniques

This talk presents practical methods for accelerating deep model inference, detailing two case studies—text QA and speech QA—along with their technical challenges, and outlines optimization strategies such as model compression, multi‑operator fusion, matrix multiplication tuning, quantization, and dynamic batching.

Deep LearningDynamic BatchingInference Acceleration

0 likes · 12 min read

Practical Acceleration of Deep Model Inference: Case Studies and Optimization Techniques

DaTaobao Tech

Jun 14, 2022 · Artificial Intelligence

Six Alibaba CVPR 2022 Papers: Summaries and Open‑Source Releases

Alibaba’s DaTaobao team presented six CVPR 2022 papers—covering video restoration, real‑time image enhancement, human‑object interaction detection, indirect illumination modeling, editable NeRFs, and robust NeRF reconstruction—with all code and models open‑sourced, and invites viewers to a live stream for detailed walkthroughs.

CVPRDeep LearningNeRF

0 likes · 5 min read

Six Alibaba CVPR 2022 Papers: Summaries and Open‑Source Releases

Xiaohongshu Tech REDtech

Jun 13, 2022 · Artificial Intelligence

Neighbor Transformer (NFormer): Robust Person Re-identification via Interactive Multi‑image Modeling

Neighbor Transformer (NFormer) introduces interactive multi‑image modeling for person re‑identification, using Landmark Agent Attention and Reciprocal Neighbor Softmax to efficiently fuse features across images, achieving state‑of‑the‑art accuracy and tighter embedding clusters on multiple benchmark datasets.

Deep Learningcomputer visionlandmark agent attention

0 likes · 8 min read

Neighbor Transformer (NFormer): Robust Person Re-identification via Interactive Multi‑image Modeling

ITPUB

Jun 9, 2022 · Artificial Intelligence

How 58’s Multi‑Label Image Recognition Boosts Semantic Search and Recommendations

This article details the design, data pipeline, model architecture, loss functions, and evaluation metrics of a large‑scale multi‑label image classification system built for 58.com, showing how it improves semantic similarity detection, recommendation, and content moderation across diverse business domains.

Deep Learningasymmetric losscomputer vision

0 likes · 18 min read

How 58’s Multi‑Label Image Recognition Boosts Semantic Search and Recommendations

Code DAO

May 31, 2022 · Artificial Intelligence

How Deep Convolutional Networks Boost Image Super-Resolution: A Paper Review

This article reviews the seminal SRCNN paper, detailing its contributions, architecture, training pipeline, hyper‑parameters, and extensive experiments that show how a shallow fully‑convolutional network achieves superior PSNR and runtime compared to traditional sparse‑coding and bicubic methods.

BenchmarkCNNDeep Learning

0 likes · 12 min read

How Deep Convolutional Networks Boost Image Super-Resolution: A Paper Review

Python Programming Learning Circle

May 29, 2022 · Artificial Intelligence

Generating Lip‑Sync Videos with PaddleGAN's Wav2Lip Model

This tutorial explains how to use the open‑source PaddleGAN Wav2Lip model to synchronise any face or avatar with arbitrary speech, covering the underlying AI principles, required installations, and step‑by‑step command‑line usage for creating high‑quality dubbing videos.

AIDeep LearningPaddleGAN

0 likes · 5 min read

Generating Lip‑Sync Videos with PaddleGAN's Wav2Lip Model

DataFunTalk

May 28, 2022 · Artificial Intelligence

Adversarial Examples for Captcha: Techniques, Applications, and Future Directions

This article presents a comprehensive overview of adversarial example research applied to captcha systems, covering the definition and history of adversarial attacks, geometric‑aware generation frameworks, FGSM‑based attack variants, experimental results, trade‑offs between image quality and attack strength, and future work such as AdvGAN integration.

AI SafetyDeep LearningFGSM

0 likes · 14 min read

Adversarial Examples for Captcha: Techniques, Applications, and Future Directions

NetEase LeiHuo Testing Center

May 27, 2022 · Artificial Intelligence

Multimodal Model for Game Frame Rate Prediction

This article explains how a multimodal deep learning model combines static and temporal game data to predict frame rates, helping identify performance bottlenecks and improve client smoothness through feature fusion, data pipelines, and real‑time inference in modern games.

AIDeep LearningFeature Engineering

0 likes · 7 min read

Multimodal Model for Game Frame Rate Prediction

Hulu Beijing

May 26, 2022 · Artificial Intelligence

Why Vector Retrieval Outperforms Keyword Search for Personalized Video Discovery

This article explains how modern video platforms combine traditional keyword retrieval with deep‑learning‑based vector retrieval, detailing model architectures, attention mechanisms, personalization features, offline experiments, and online A/B results that show significant improvements in recall, relevance, and user experience.

Deep LearningVector Retrievalinformation retrieval

0 likes · 18 min read

Why Vector Retrieval Outperforms Keyword Search for Personalized Video Discovery

AntTech

May 24, 2022 · Artificial Intelligence

WPipe: Group‑Based Interleaved Pipeline Parallelism for Large‑Scale DNN Training

The paper introduces WPipe, a group‑based interleaved pipeline parallelism method that reduces memory overhead and weight‑update latency compared with PipeDream‑2BW, achieving up to 1.4× speed‑up and 36% lower memory usage while preserving model accuracy on large‑scale DNNs.

Deep LearningPipeline ParallelismTraining Throughput

0 likes · 13 min read

WPipe: Group‑Based Interleaved Pipeline Parallelism for Large‑Scale DNN Training

DataFunTalk

May 23, 2022 · Artificial Intelligence

A Survey of Deep Matching Models for Search and Recommendation

This article surveys recent deep learning approaches for matching in search and recommendation systems, presenting a unified view of matching, categorizing methods into representation learning and matching function learning, and detailing model architectures from input to output layers, while highlighting broader applications such as QA and image captioning.

Deep LearningSearchSurvey

0 likes · 4 min read

A Survey of Deep Matching Models for Search and Recommendation

DataFunTalk

May 14, 2022 · Artificial Intelligence

Introducing DGL: An Efficient, User‑Friendly, Open Graph Deep Learning Platform

This article presents an overview of graph data and graph neural networks, explains the core concepts of message‑passing GNNs, highlights DGL’s flexible API, high‑performance system design, large‑scale training capabilities and open‑source ecosystem, and outlines future plans and community resources.

DGLDeep Learninggraph data

0 likes · 17 min read

Introducing DGL: An Efficient, User‑Friendly, Open Graph Deep Learning Platform

DataFunTalk

May 14, 2022 · Artificial Intelligence

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

The 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse and Imbalanced Data (DLP‑KDD 2022) invites submissions on deep‑learning systems, data representation, and user modeling for large‑scale sparse data, with a deadline of May 26, 2022 and acceptance notifications by June 20, 2022.

AIDeep LearningSparse Data

0 likes · 5 min read

Call for Papers: 4th International Workshop on Deep Learning Practice for High‑Dimensional Sparse Data (DLP‑KDD 2022)

Code DAO

May 12, 2022 · Artificial Intelligence

How Activation Functions Work in Deep Learning

This article explains the role of activation functions in deep learning, covering their definition, why they are needed, the main categories—including linear, binary step, and various non‑linear functions such as Sigmoid, TanH, ReLU, Leaky ReLU, ELU, Softmax and Swish—along with each function's mathematical form, advantages, disadvantages, and practical usage recommendations.

Deep LearningNeural NetworkReLU

0 likes · 13 min read

How Activation Functions Work in Deep Learning

Baidu Geek Talk

May 6, 2022 · Artificial Intelligence

Artificial Intelligence Development History and Pre‑training Model Trends

From the 1940s birth of computers to today's ultra‑large pre‑training models like Baidu’s ERNIE 3.0, AI has progressed through three development waves, now driven by algorithms, compute and data, with pre‑training lowering application barriers and evolving toward larger, multimodal, and more generalizable systems.

Artificial IntelligenceDeep Learningmachine learning

0 likes · 11 min read

Artificial Intelligence Development History and Pre‑training Model Trends

DataFunTalk

May 4, 2022 · Artificial Intelligence

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

This article reviews the evolution of recommendation models from early collaborative filtering to modern deep learning approaches, discusses core challenges such as CTR prediction, outlines user‑behavior and combination‑feature modeling techniques, introduces large‑embedding training and continuous‑feature embedding methods like AutoDis, and presents distributed training frameworks such as ScaleFreeCTR, concluding with future research directions.

CTR predictionDeep LearningEmbedding

0 likes · 21 min read

Advances in Recommendation Models: CTR Prediction, Continuous Feature Embedding, Interaction Modeling, and Distributed Training

Tencent Cloud Developer

Apr 27, 2022 · Artificial Intelligence

Alignment-Uniformity Representation Learning for Zero-shot Video Classification (AURL)

The AURL framework, presented by Pu Shi, introduces alignment‑uniformity aware representation learning for zero‑shot video classification, achieving up to 28 % top‑1 accuracy gains on UCF101 and HMDB51, and has already boosted business metrics in Tencent’s advertising, search, and video‑channel recommendation systems.

Deep Learningalignmentcomputer vision

0 likes · 19 min read

Alignment-Uniformity Representation Learning for Zero-shot Video Classification (AURL)

Alibaba Cloud Developer

Apr 26, 2022 · Artificial Intelligence

Unlocking Vision AI: Inside Alibaba’s EasyCV All‑in‑One Self‑Supervised & Transformer Framework

EasyCV is Alibaba’s open‑source, PyTorch‑based visual modeling platform that unifies self‑supervised learning and Transformer techniques, offering a comprehensive algorithm suite, pre‑trained models, high‑performance training/inference optimizations, extensible architecture, and seamless cloud deployment for a wide range of computer‑vision tasks.

AI FrameworkAlibabaDeep Learning

0 likes · 16 min read

Unlocking Vision AI: Inside Alibaba’s EasyCV All‑in‑One Self‑Supervised & Transformer Framework

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

360 Quality & Efficiency

Apr 22, 2022 · Artificial Intelligence

Audio Quality Assessment Using a BiLSTM Deep Learning Model

This article presents a no‑reference audio quality assessment system that leverages a bidirectional LSTM network to extract spectral features via FFT and predict perceptual scores, describing the architecture, technical advantages, data preparation, loss design, and TensorFlow implementation.

BiLSTMDeep LearningSignal Processing

0 likes · 8 min read

Audio Quality Assessment Using a BiLSTM Deep Learning Model

Programmer DD

Apr 18, 2022 · Artificial Intelligence

Unlocking Captcha Secrets: How the Open‑Source ddddocr Python Library Works

This article introduces the open‑source Python library ddddocr, explains its evolution from version 1.2.0 to 1.4.3—including OCR, target detection, and slider recognition features—and shows how it leverages deep‑learning and OpenCV to simplify captcha solving for developers.

CaptchaDeep LearningOCR

0 likes · 4 min read

Unlocking Captcha Secrets: How the Open‑Source ddddocr Python Library Works

GuanYuan Data Tech Team

Apr 14, 2022 · Artificial Intelligence

Mastering Time Series Forecasting: From Moving Averages to Transformers

Time series forecasting, essential across weather, finance, and commerce, involves tasks like classification, clustering, anomaly detection, and especially prediction; this article explores its definitions, evaluation metrics, traditional methods, machine‑learning approaches, deep‑learning models such as TFT, and emerging AutoML tools, offering practical insights and best practices.

AutoMLDeep LearningGBDT

0 likes · 27 min read

Mastering Time Series Forecasting: From Moving Averages to Transformers

DataFunSummit

Apr 12, 2022 · Artificial Intelligence

Intelligent Auction Mechanisms for Alibaba Display Advertising: AIDA Framework, Deep GSP, and Neural Auction

This article presents the evolution of Alibaba's display advertising auction mechanisms, introducing the AIDA decision‑allocation framework, the Deep GSP multi‑objective smart auction, and the end‑to‑end Neural Auction, while discussing their economic theory, engineering platformization, business impact, and future research directions.

AIDeep Learningauction

0 likes · 18 min read

Intelligent Auction Mechanisms for Alibaba Display Advertising: AIDA Framework, Deep GSP, and Neural Auction

Kuaishou Tech

Apr 11, 2022 · Artificial Intelligence

Kuaishou's Custom Video Matting Solution: Interactive Object Segmentation for Mobile Creators

Kuaishou's audio‑video technology team presents a self‑developed custom video matting system that combines foreground, interactive, and video object segmentation to let creators extract arbitrary subjects without green screens, featuring adaptive cropping, multi‑stage training, and deployment across Android and iOS devices.

Deep LearningKuaishoucomputer vision

0 likes · 15 min read

Kuaishou's Custom Video Matting Solution: Interactive Object Segmentation for Mobile Creators

Baidu Geek Talk

Apr 8, 2022 · Artificial Intelligence

Golang Object Pool for Reducing GC Pressure, FFmpeg Concurrency Control, and Paddle Static vs. Dynamic Graphs

The article explains how Go's lock‑free sync.Pool can cut garbage‑collection overhead, shows practical FFmpeg thread‑parameter tuning that balances CPU use and latency for video filtering versus encoding, and compares PaddlePaddle's static and dynamic graph modes, including debugging tips and conversion to static.

ConcurrencyDeep LearningDynamic Graph

0 likes · 13 min read

Golang Object Pool for Reducing GC Pressure, FFmpeg Concurrency Control, and Paddle Static vs. Dynamic Graphs

Kuaishou Large Model

Apr 6, 2022 · Artificial Intelligence

How Transformers Revolutionize Image Style Transfer: Introducing StyTr²

This article reviews the limitations of traditional CNN‑based image stylization, explains how Transformer architectures overcome these issues with global context and self‑attention, and presents the novel StyTr² method with content‑aware positional encoding that achieves superior, detail‑preserving style transfer results.

Deep LearningTransformercomputer vision

0 likes · 8 min read

How Transformers Revolutionize Image Style Transfer: Introducing StyTr²

Kuaishou Tech

Apr 6, 2022 · Artificial Intelligence

StyTr²: A Transformer‑Based Approach for Image Style Transfer

The paper proposes StyTr², a Transformer‑based image style transfer method that uses content‑aware positional encoding to preserve details and improve feature representation, achieving high‑quality stylization with better content structure and style patterns.

Deep Learningcomputer visioncontent-aware positional encoding

0 likes · 7 min read

StyTr²: A Transformer‑Based Approach for Image Style Transfer

DataFunTalk

Apr 6, 2022 · Artificial Intelligence

AIDA Advertising Intelligent Decision and Allocation Framework: Evolution of Smart Auction Mechanisms

This article introduces the AIDA framework for Alibaba's display advertising, detailing the business background, multi‑objective optimization challenges, the design of Deep GSP and Neural Auction mechanisms powered by deep learning and reinforcement learning, and outlines future technical and platform directions while also announcing recruitment opportunities.

AIAdvertisingDeep Learning

0 likes · 16 min read

AIDA Advertising Intelligent Decision and Allocation Framework: Evolution of Smart Auction Mechanisms

NetEase LeiHuo Testing Center

Apr 1, 2022 · Artificial Intelligence

Learning OCR for Game Text Recognition: From Data Preparation to CRNN Model Training

This article documents the author’s step‑by‑step journey of building an OCR system for recognizing Chinese characters in a card‑game UI, covering game selection, technical background, data generation, deep‑learning model training with CRNN, real‑image data collection, optimization attempts, and final performance evaluation.

CRNNDeep LearningEasyOCR

0 likes · 15 min read

Learning OCR for Game Text Recognition: From Data Preparation to CRNN Model Training

Python Programming Learning Circle

Mar 31, 2022 · Artificial Intelligence

Comprehensive PyTorch Code Snippets: Configuration, Tensor Operations, Model Definition, Training, and Best Practices

This article provides a thorough collection of commonly used PyTorch code snippets covering environment setup, reproducibility, GPU configuration, tensor manipulation, model building, data preprocessing, training and evaluation loops, custom loss functions, regularization techniques, learning‑rate scheduling, checkpointing, and practical tips for efficient deep‑learning development.

Deep LearningGPUPython

0 likes · 37 min read

Comprehensive PyTorch Code Snippets: Configuration, Tensor Operations, Model Definition, Training, and Best Practices

Cyber Elephant Tech Team

Mar 30, 2022 · Artificial Intelligence

Can AI Make Real-Life Invisibility Cloaks? Inside the STTN Video Restoration Breakthrough

This article reviews the challenges of video inpainting, surveys traditional methods, and introduces the Spatial‑Temporal Transformer Network (STTN) that leverages multi‑scale attention and a Temporal Patch‑GAN discriminator, detailing its architecture, loss functions, training on Youtube‑VOS, and impressive restoration results.

AI video restorationDeep LearningVideo Inpainting

0 likes · 10 min read

Can AI Make Real-Life Invisibility Cloaks? Inside the STTN Video Restoration Breakthrough

Baidu Geek Talk

Mar 28, 2022 · Artificial Intelligence

Robust Input Visualization Methods for Vision Transformers

The paper proposes a robust Grad‑CAM‑inspired visualization for Vision Transformers that combines attention weights and gradients to generate class‑specific saliency maps, demonstrates superior alignment with discriminative regions across ViT, Swin and Volo models, and shows a 76% false‑positive reduction in Baidu’s porn‑content risk control system.

Deep LearningGrad-CAMInput Visualization

0 likes · 11 min read

Robust Input Visualization Methods for Vision Transformers

DataFunSummit

Mar 26, 2022 · Artificial Intelligence

Deep Learning‑Based Design of Financial Index Funds Using Graph Neural Networks

This talk presents a deep‑learning framework that formulates financial index‑fund construction as a sparse portfolio optimization problem, solves the mixed‑integer programming via a two‑stage graph‑neural‑network pipeline, and demonstrates superior tracking performance and scalability on large‑scale index datasets.

AI FinanceDeep Learningfinancial index funds

0 likes · 16 min read

Deep Learning‑Based Design of Financial Index Funds Using Graph Neural Networks

Kuaishou Large Model

Mar 25, 2022 · Artificial Intelligence

How Kuaishou Y‑Tech Achieves Real‑Time High‑Fidelity Wrinkle Removal with AI

This article explains how Kuaishou Y‑Tech combines image inpainting, semantic editing, and advanced GAN techniques to accurately locate and remove facial wrinkles—especially neck wrinkles—while preserving realistic skin texture and achieving high‑quality results suitable for production deployment.

Deep LearningGaNcomputer vision

0 likes · 17 min read

How Kuaishou Y‑Tech Achieves Real‑Time High‑Fidelity Wrinkle Removal with AI

Laiye Technology Team

Mar 25, 2022 · Artificial Intelligence

Laiye OCR Error‑Correction Model: Architecture, Implementation, and Evaluation

This article describes Laiye's OCR error‑correction system, detailing the background challenges of Chinese character recognition, the analysis of three possible solutions, the chosen post‑processing approach, model architecture, training data, loss design, online inference, and experimental results showing a measurable performance boost.

Chinese textDeep LearningError Correction

0 likes · 13 min read

Laiye OCR Error‑Correction Model: Architecture, Implementation, and Evaluation

Meituan Technology Team

Mar 24, 2022 · Artificial Intelligence

Cyclic Generative Adversarial Networks for Probability Density Estimation – Academic Salon by Tsinghua University & Meituan Digital Life

The Tsinghua‑Meituan Digital Life Joint Research Institute’s academic salon will feature Associate Professor Jiang Rui presenting a cyclic generative adversarial network for probability density estimation, demonstrating how merging statistical models with deep‑learning techniques can solve core statistical problems and foster industry‑academia innovation.

Artificial IntelligenceDeep LearningGenerative Adversarial Networks

0 likes · 4 min read

Cyclic Generative Adversarial Networks for Probability Density Estimation – Academic Salon by Tsinghua University & Meituan Digital Life

Open Source Linux

Mar 23, 2022 · Artificial Intelligence

What Is Machine Learning? A Complete Guide to Concepts, Evolution, and Algorithms

This article provides a comprehensive overview of machine learning, explaining its definition, relationship to AI, workflow, historical evolution, major paradigms, real‑world applications, and the most common algorithms with practical usage tips.

AlgorithmsArtificial IntelligenceDeep Learning

0 likes · 14 min read

What Is Machine Learning? A Complete Guide to Concepts, Evolution, and Algorithms

JD Cloud Developers

Mar 21, 2022 · Artificial Intelligence

ViTAEv2 Breaks ImageNet Real Record with 91.2% Accuracy – How a 600M‑Parameter Model Redefines Few‑Shot Learning

JD Research Institute and the University of Sydney introduced ViTAEv2, a 600‑million‑parameter deep learning model that achieved a world‑leading 91.2% top‑1 accuracy on ImageNet Real without external data, demonstrating strong few‑shot learning, reducing labeling costs, and promising advances across many computer‑vision tasks.

AI modelDeep LearningImageNet

0 likes · 4 min read

ViTAEv2 Breaks ImageNet Real Record with 91.2% Accuracy – How a 600M‑Parameter Model Redefines Few‑Shot Learning

DataFunSummit

Mar 16, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

This article presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage recall, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network (SearchGCN), a transformer‑based synonym generation system, and a joint index‑training approach that integrates product quantization to improve recall accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model

0 likes · 17 min read

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Joint Index Training

DataFunTalk

Mar 16, 2022 · Artificial Intelligence

Parameter-Efficient Sparsity Training for the PLUG Large-Scale Language Model

This article presents the PLUG 270‑billion‑parameter Chinese language model and introduces a parameter‑efficient sparsity training (PST) framework that combines unstructured and structured pruning with low‑rank decomposition to dramatically reduce model size while preserving downstream performance.

Deep LearningLarge Language ModelsPLUG

0 likes · 13 min read

Parameter-Efficient Sparsity Training for the PLUG Large-Scale Language Model

Tencent Cloud Developer

Mar 15, 2022 · Artificial Intelligence

Comprehensive Overview of Ranking Models in Recommendation Systems

The article provides a thorough guide to ranking in recommendation systems, detailing the pipeline architecture, sample handling challenges, extensive feature engineering categories, the evolution from collaborative filtering to deep and attention‑based models, and key optimization trade‑offs between memorization, generalization, and efficient user‑interest modeling.

CTR predictionDeep LearningFeature Engineering

0 likes · 19 min read

Comprehensive Overview of Ranking Models in Recommendation Systems

DataFunTalk

Mar 12, 2022 · Artificial Intelligence

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

This article presents a comprehensive overview of NetEase Cloud Music's advertising system, detailing its architecture, core challenges, CTR and CVR prediction models, feature engineering, model evolution from LR to deep learning, user vector modeling, and practical recommendations for improving ad performance.

AdvertisingCTR predictionDeep Learning

0 likes · 15 min read

NetEase Cloud Music Advertising System: Algorithm Practice and Model Evolution

DeWu Technology

Mar 11, 2022 · Artificial Intelligence

Deep Learning in Face Recognition

The article surveys deep‑learning‑based face‑recognition systems, detailing detection, preprocessing, and recognition pipelines, describing evaluation metrics such as TAR, FAR, and Rank‑K, reviewing major datasets like LFW, MS‑Celeb‑1M and VGGFace2, and comparing leading architectures—including FaceNet, CenterLoss, SphereFace and InsightFace—while highlighting their strengths, limitations, real‑world applications, and seminal research references.

AIDeep Learningdatasets

0 likes · 14 min read

DaTaobao Tech

Mar 11, 2022 · Artificial Intelligence

How Alibaba’s MNN Engine Achieves 350% CPU Speedup and Sparse Acceleration

Alibaba’s MNN, a lightweight high‑performance deep‑learning inference engine, earned top honors in China’s 2022 “Science & Innovation China” awards, and delivers impressive gains such as 350% speedup on X86 CPUs, 2.1‑2.3× acceleration on ARM with sparse models, plus integrated OpenCV/Numpy functionality for edge AI deployment.

AI deploymentAlibabaDeep Learning

0 likes · 4 min read

How Alibaba’s MNN Engine Achieves 350% CPU Speedup and Sparse Acceleration

DataFunTalk

Mar 9, 2022 · Artificial Intelligence

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training

The talk presents JD's end‑to‑end semantic search recall pipeline, covering multi‑stage retrieval, a dual‑tower embedding model with multi‑head attention, a heterogeneous graph neural network for low‑frequency items, automatic synonym generation via transformer models, and a joint training approach that integrates product quantization directly into the model to improve accuracy and efficiency.

Deep LearningGraph Neural Networkdual-tower model

0 likes · 16 min read

Semantic Search Recall Techniques at JD: Dual‑Tower Model, Graph Model, Synonym Recall, and Index Joint Training

DataFunSummit

Mar 6, 2022 · Artificial Intelligence

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

This article traces the development of embedding methods—from the early word2vec model through item2vec, DeepWalk, Node2vec, EGES, HERec, GraphRT, and target‑fitting approaches like DSSM and YouTube recommendation—highlighting how sequence‑construction and target‑fitting paradigms have shaped modern recommendation systems and AI applications.

Deep LearningEmbeddingItem2Vec

0 likes · 26 min read

The Evolution of Embedding Techniques: From Word2Vec to Graph Neural Networks

Alibaba Cloud Native

Mar 5, 2022 · Cloud Native

How Fluid Accelerates Cloud‑Native Deep Learning Training

Fluid, an open‑source CNCF project co‑developed by Alibaba Cloud and Nanjing University, introduces a dataset abstraction and elastic caching architecture that automatically optimizes I/O for cloud‑native deep‑learning training jobs, and its research was accepted as a full paper at the prestigious ICDE 2022 conference.

Data AccelerationDeep LearningICDE

0 likes · 6 min read

How Fluid Accelerates Cloud‑Native Deep Learning Training

IT Services Circle

Mar 2, 2022 · Artificial Intelligence

Curated Open‑Source Face Recognition Projects Overview

This article presents a curated collection of open‑source face recognition projects—including OpenFace, face_recognition, InsightFace, RetinaFace, SCRFD, FaceNet, Deepface, and CompreFace—detailing their features, GitHub stars, usage examples, and code snippets for Python and TensorFlow implementations.

Artificial IntelligenceDeep LearningPython

0 likes · 5 min read

Curated Open‑Source Face Recognition Projects Overview

DataFunSummit

Mar 1, 2022 · Artificial Intelligence

Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases

This article details Alibaba's exploration of intelligent supply‑chain forecasting, covering scenario classification, three generations of prediction algorithms, the self‑developed Falcon model, performance evaluation, and real‑world cases such as Double 11 and live‑streaming, highlighting challenges and practical solutions.

AIDeep LearningTime-series

0 likes · 18 min read

Alibaba's Smart Supply‑Chain Forecasting: Scenarios, Algorithm R&D, and Application Cases

Python Programming Learning Circle

Feb 28, 2022 · Artificial Intelligence

Integrating MobileNet Series into YOLOv4 for Efficient Object Detection

This guide explains how to replace YOLOv4's CSPdarknet53 backbone with MobileNetV1, V2, or V3 networks, detailing the architecture analysis, code implementations, training setup, dataset preparation, and inference procedures for building a lightweight object detection model.

Deep LearningMobileNetYOLOv4

0 likes · 26 min read

Integrating MobileNet Series into YOLOv4 for Efficient Object Detection

DataFunSummit

Feb 26, 2022 · Artificial Intelligence

Graph-Based Sparse Behavior Recall Models for Content Recommendation

This article presents a comprehensive study of graph‑based recall techniques for content recommendation, detailing how knowledge‑graph‑augmented user‑behavior graphs and novel attention‑driven models such as GADM, SGGA, and SGGGA improve performance for users with sparse interaction histories.

Deep LearningKnowledge GraphRecommendation Systems

0 likes · 11 min read

Graph-Based Sparse Behavior Recall Models for Content Recommendation

iQIYI Technical Product Team

Feb 25, 2022 · Artificial Intelligence

Short Video Content Tagging: Multimodal AI Model Framework and Applications

The framework tags short videos by fusing text, image and audio‑video features through specialized extraction, classification, generative and retrieval modules, then ranking candidates with a multimodal BERT model, delivering accurate, business‑specific tags that boost recommendation, search and advertising.

Deep Learningcontent taggingmultimodal AI

0 likes · 10 min read

Short Video Content Tagging: Multimodal AI Model Framework and Applications

Kuaishou Tech

Feb 18, 2022 · Game Development

Motion Retargeting Techniques for High‑Quality Virtual Character Driving

The article surveys motion retargeting, describing its importance for virtual characters, outlining traditional geometric methods and recent deep‑learning approaches, presenting a customized Interaction Mesh‑based solution from Kuaishou Y‑tech, and discussing performance, limitations, and future research directions.

Deep Learningmotion retargetingvirtual characters

0 likes · 11 min read

Motion Retargeting Techniques for High‑Quality Virtual Character Driving

DataFunTalk

Feb 18, 2022 · Artificial Intelligence

Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi‑source Behavior Modeling, and Model Design

This talk presents Alibaba's travel intent prediction system, detailing the unique challenges of low‑frequency, multi‑source travel behavior, the multi‑granular CNN and time‑attention model architecture, experimental comparisons with baselines, and how integrated user interest modeling improves recommendation performance.

Deep Learningattentionmachine learning

0 likes · 11 min read

Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi‑source Behavior Modeling, and Model Design

Baidu Geek Talk

Feb 17, 2022 · Artificial Intelligence

AI-Powered Sports Video Applications: Figure Skating Action Recognition, Multimodal Classification, and Football Highlight Clipping

The article showcases three AI‑driven sports video solutions—real‑time figure‑skating action recognition with ST‑GCN, multimodal video classification merging text, image and audio via ERNIE and TextCNN, and automated football highlight clipping using TSN‑BMN‑LSTM—each achieving over 85% accuracy, fully open‑source on PaddlePaddle with one‑click notebooks and a live developer session.

AIDeep LearningMultimodal Classification

0 likes · 8 min read

AI-Powered Sports Video Applications: Figure Skating Action Recognition, Multimodal Classification, and Football Highlight Clipping

HomeTech

Feb 15, 2022 · Artificial Intelligence

Horovod Distributed Deep Learning Training: Architecture, Performance, and Kubernetes Deployment

This article provides a comprehensive overview of Horovod, Uber's open-source distributed deep learning framework, covering its architecture, communication mechanisms, performance benchmarks, and deployment on Kubernetes and Spark for accelerated multi-GPU training.

Deep LearningGPU AccelerationHorovod

0 likes · 17 min read

Horovod Distributed Deep Learning Training: Architecture, Performance, and Kubernetes Deployment

DataFunSummit

Feb 14, 2022 · Artificial Intelligence

Evolution of 58 Local Service Recommendation Algorithms and Future Directions

This article presents a comprehensive overview of 58's local service recommendation system, detailing the characteristics of its recommendation scenarios, the evolution of tag and post recommendation pipelines, the underlying deep‑learning models such as Bi‑LSTM, ATRank, DeepFM and ESMM, and outlines future research directions.

ATRankCTRCVR

0 likes · 16 min read

Evolution of 58 Local Service Recommendation Algorithms and Future Directions

Kuaishou Large Model

Feb 11, 2022 · Artificial Intelligence

How Motion Retargeting Powers Real‑Time Virtual Humans: Techniques & Insights

Motion retargeting, also known as motion adaptation, transfers source motion to arbitrary virtual characters while preserving semantic features and natural flow, and this article reviews traditional geometry‑based methods, recent deep‑learning approaches, and Kuaishou Y‑Tech’s optimized pipeline that balances quality, contact preservation, and real‑time performance.

AnimationDeep Learningcomputer graphics

0 likes · 13 min read

How Motion Retargeting Powers Real‑Time Virtual Humans: Techniques & Insights

DataFunTalk

Feb 5, 2022 · Artificial Intelligence

Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions

This article presents a comprehensive overview of 58 Local Service's recommendation system, detailing the diverse recommendation scenarios, challenges such as information homogeneity and complex user structures, the multi‑stage recall and ranking pipelines, model evolutions from statistical methods to deep learning, and future work to improve data quality and model efficiency.

ATRankCTRCVR

0 likes · 15 min read

Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions

DataFunTalk

Feb 2, 2022 · Artificial Intelligence

UGC Sentiment Analysis Solutions and Applications in Taobao

This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.

Deep LearningKnowledge GraphSentiment Analysis

0 likes · 16 min read

UGC Sentiment Analysis Solutions and Applications in Taobao

MaGe Linux Operations

Jan 30, 2022 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?

This article compares PyTorch and TensorFlow in 2022 across model availability, deployment ease, and ecosystem support, using data from HuggingFace, research papers, and industry tools, and offers tailored recommendations for industry engineers, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningModel Deployment

0 likes · 20 min read

PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?

DataFunSummit

Jan 29, 2022 · Artificial Intelligence

Survey of Model Pruning and Quantization Techniques for Deep Learning

This article provides a comprehensive overview of recent advances in deep learning model compression, focusing on pruning methods—including unstructured, structured, filter-wise, channel-wise, shape-wise, and stripe-wise approaches—and quantization techniques such as linear, non‑linear, clustering, power‑of‑two, binary, and 8‑bit quantization, while discussing evaluation criteria, sparsity ratios, fine‑tuning, and training‑aware quantization.

Deep LearningQuantizationmodel compression

0 likes · 23 min read

Survey of Model Pruning and Quantization Techniques for Deep Learning

Xianyu Technology

Jan 29, 2022 · Artificial Intelligence

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Xianyu search recall stage upgraded from simple text matching to semantic vector retrieval using DSSM with Electra‑Small, query graph attention, and behavior‑based I2I, adding structured attributes and OCR tags, improving AUC to 0.824 and HitRate@10 to 90.1%, boosting purchase metrics by up to 4%.

Deep LearningVector RetrievalXianyu

0 likes · 17 min read

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Bilibili Tech

Jan 28, 2022 · Artificial Intelligence

Real-CUGAN: An Open‑Source AI Super‑Resolution Model for Anime Video Upscaling

Real‑CUGAN is an open‑source AI super‑resolution model that upscales anime video up to 4× using a million‑patch, frequency‑domain‑supervised dataset, delivering faster inference than Real‑ESRGAN, seamless Waifu2x compatibility, and superior texture, line and artifact handling, with code released on GitHub.

AI super-resolutionDeep LearningReal-CUGAN

0 likes · 8 min read

Real-CUGAN: An Open‑Source AI Super‑Resolution Model for Anime Video Upscaling

Laiye Technology Team

Jan 28, 2022 · Artificial Intelligence

Survey of Model Compression and Quantization Techniques for Deep Neural Networks

This article provides a comprehensive overview of deep learning model compression and acceleration methods, detailing pruning strategies, various pruning types, evaluation criteria, sparsity ratios, fine‑tuning procedures, as well as linear and non‑linear quantization approaches, their implementations, and practical considerations.

Deep LearningEfficiencyQuantization

0 likes · 26 min read

Survey of Model Compression and Quantization Techniques for Deep Neural Networks

Baobao Algorithm Notes

Jan 28, 2022 · Artificial Intelligence

How Pre‑Training Evolved: From word2vec to MAE Across NLP and CV

This article traces the history of deep‑learning pre‑training techniques, comparing the parallel developments in natural‑language processing and computer vision—from early word2vec and bag‑of‑words models through ELMo and BERT to recent transformer‑based vision models like iGPT, ViT, BEiT and MAE—highlighting key innovations, challenges, and the convergence of the two fields.

Deep LearningMAENLP

0 likes · 20 min read

How Pre‑Training Evolved: From word2vec to MAE Across NLP and CV

DataFunTalk

Jan 23, 2022 · Artificial Intelligence

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

This article presents a systematic study of recall techniques for new‑user cold‑start in content recommendation, describing a baseline two‑tower model, a Dual Attention Network (DAN) fusion approach, and an enhanced Contextual‑Gate DAN that dynamically balances content and product sequences, together with offline and online evaluation results and future directions.

Deep LearningUser Embeddingcold-start

0 likes · 12 min read

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

Python Programming Learning Circle

Jan 18, 2022 · Artificial Intelligence

Fashion MNIST Image Classification Using TensorFlow 2.x in Python

This tutorial demonstrates how to load the Fashion MNIST dataset, explore and preprocess the images, build and compile a neural network with TensorFlow 2.x, train the model, evaluate its accuracy, and use the trained model to make predictions on clothing images, providing complete Python code examples throughout.

Deep LearningFashion-MNISTImage Classification

0 likes · 16 min read

Fashion MNIST Image Classification Using TensorFlow 2.x in Python

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 ProcessingDeep LearningSpeech Recognition

0 likes · 40 min read

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

Code DAO

Jan 15, 2022 · Artificial Intelligence

How Intel BF16 with IPEX and oneDNN Boosts PyTorch Performance

This article explains how Intel and Facebook's BF16 support, combined with the Intel Extension for PyTorch (IPEX) and oneDNN, automates type and layout conversions and adds graph‑fusion optimizations, delivering 1.4×‑4.3× inference and up to 2.4× training speedups on Xeon CPUs for models such as DLRM, BERT‑Large, and ResNext‑101‑32x4d.

BF16CPU accelerationDeep Learning

0 likes · 13 min read

How Intel BF16 with IPEX and oneDNN Boosts PyTorch Performance

Baobao Algorithm Notes

Jan 14, 2022 · Artificial Intelligence

Boosting BERT Text Classification with Label Embedding: How It Works

The paper proposes a simple yet effective method that fuses label embeddings into BERT, enhancing text‑classification performance without increasing computational cost, and validates the approach across six benchmark datasets, also exploring tf‑idf‑based label augmentation and the impact of using [SEP] versus no‑[SEP] inputs.

BERTDeep LearningNLP

0 likes · 8 min read

Boosting BERT Text Classification with Label Embedding: How It Works

Python Programming Learning Circle

Jan 11, 2022 · Artificial Intelligence

Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage

This article explains how to configure and use PyTorch optimizers, their attributes and methods, and demonstrates various learning‑rate scheduling techniques—including manual updates and built‑in schedulers such as LambdaLR, StepLR, MultiStepLR, ExponentialLR, CosineAnnealingLR, and ReduceLROnPlateau—through clear code examples.

Deep LearningPyTorchScheduler

0 likes · 14 min read

Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage

DataFunSummit

Jan 10, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI applicationsDeep Learningapproximate nearest neighbor

0 likes · 21 min read

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

DataFunTalk

Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep LearningFeature EngineeringRecommendation Systems

0 likes · 12 min read

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

Kuaishou Tech

Jan 7, 2022 · Artificial Intelligence

Transcoded Video Restoration by Temporal‑Spatial Auxiliary Network – AAAI 2022 Paper Overview

The article summarizes a AAAI 2022 paper by Kuaishou's audio‑video algorithm team and Xidian University that introduces a deep‑learning‑based video compression‑artifact restoration method using temporal‑spatial auxiliary supervision, achieving significant PSNR/SSIM gains on transcoded videos without increasing bitrate.

AAAI 2022Deep Learningcompression artifact removal

0 likes · 6 min read

Transcoded Video Restoration by Temporal‑Spatial Auxiliary Network – AAAI 2022 Paper Overview

Baobao Algorithm Notes

Jan 7, 2022 · Interview Experience

Essential Transformer Interview Cheat Sheet: 11 Must‑Know Q&A

This concise guide presents eleven frequently asked Transformer interview questions with clear, English explanations covering self‑attention formulas, scaling, alternative designs, LayerNorm vs. BatchNorm, positional embeddings, multi‑head mechanisms, and BPE tokenization, helping candidates deliver solid, theory‑backed answers.

BERTDeep LearningLayerNorm

0 likes · 6 min read

Essential Transformer Interview Cheat Sheet: 11 Must‑Know Q&A

Laiye Technology Team

Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI applicationsDeep LearningHNSW

0 likes · 21 min read

JD Cloud Developers

Jan 4, 2022 · Artificial Intelligence

How JD’s Vega v1 Model Dominated GLUE Benchmark, Surpassing Human Performance

JD Explore’s Vega v1 model topped the GLUE benchmark with a 91.3 average score, outperforming Microsoft, Facebook, and Stanford across multiple NLP tasks, including first‑ever human‑level results on sentiment analysis and coreference, showcasing JD’s leading position in deep‑learning research.

AI researchDeep LearningGLUE benchmark

0 likes · 3 min read

How JD’s Vega v1 Model Dominated GLUE Benchmark, Surpassing Human Performance

Code DAO

Dec 31, 2021 · Artificial Intelligence

Why RegNet Is the Most Flexible Architecture for Computer Vision

RegNet introduces a scalable design space defined by quantized linear functions, enabling flexible trade‑offs between accuracy, efficiency, and mobile deployment, and demonstrates superior performance compared with ResNet, EfficientNet, and other mobile‑optimized networks.

Deep LearningDesign SpaceRegNet

0 likes · 7 min read

Why RegNet Is the Most Flexible Architecture for Computer Vision

Laiye Technology Team

Dec 31, 2021 · Artificial Intelligence

Overview of Table Recognition Techniques and Practical Implementation

This article reviews the challenges of extracting structured table data from images, compares two‑stage and end‑to‑end OCR approaches, evaluates four state‑of‑the‑art table‑recognition models (SPLERGE, CascadeTabNet, TableMASTER, UnetTable), and presents a practical deployment workflow with performance metrics.

AIDeep LearningOCR

0 likes · 14 min read

Overview of Table Recognition Techniques and Practical Implementation

Python Programming Learning Circle

Dec 27, 2021 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework to Choose?

An in‑depth 2022 comparison of PyTorch and TensorFlow evaluates model availability, deployment ease, and ecosystem support, showing PyTorch dominates research while TensorFlow excels in deployment, and offers tailored recommendations for industry professionals, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningPyTorch

0 likes · 20 min read

PyTorch vs TensorFlow in 2022: Which Framework to Choose?

Code DAO

Dec 24, 2021 · Artificial Intelligence

Understanding Neural Network Predictions with Integrated Gradients

This article introduces the Integrated Gradients (IG) method for explaining deep neural networks, compares it with saliency maps and Shapley‑based approaches, discusses its axiomatic foundations, and provides a step‑by‑step guide to implementing IG using the open‑source TruLens library, including custom baselines and attribution measures.

Attribution MethodsDeep LearningIntegrated Gradients

0 likes · 14 min read

Understanding Neural Network Predictions with Integrated Gradients

Code DAO

Dec 23, 2021 · Artificial Intelligence

Permutation‑Invariant PIUnet Boosts Multi‑Temporal Satellite Image Super‑Resolution

The article explains how satellite images suffer from limited spatial resolution, why the ordering of multi‑temporal frames is irrelevant, and how the PIUnet model introduces permutation‑invariant equivariant layers to achieve state‑of‑the‑art super‑resolution efficiently, winning the AI4EO challenge.

Deep LearningPIUnetSatellite Imagery

0 likes · 6 min read

Permutation‑Invariant PIUnet Boosts Multi‑Temporal Satellite Image Super‑Resolution

Code DAO

Dec 23, 2021 · Artificial Intelligence

Deep Siamese Network for Measuring Similarity of ECG Signals

This article presents an automated neural‑network framework based on a deep Siamese architecture to learn similarity representations between ECG recordings, covering ECG fundamentals, exploratory data analysis, signal preprocessing, model construction with Keras, and demonstrates how the trained network yields similarity scores applicable to broader signal‑matching tasks.

Deep LearningECGKeras

0 likes · 10 min read

Deep Siamese Network for Measuring Similarity of ECG Signals

DataFunTalk

Dec 22, 2021 · Artificial Intelligence

Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework

This article explains why traditional DAU metrics are insufficient, introduces survival analysis fundamentals and key functions, demonstrates how Kaplan‑Meier curves can characterize user activity, and presents KwaiSurvival—a deep‑learning‑based survival modeling suite with DeepSurv, DeepHit and N‑MTLR models—for practical user‑engagement and churn‑prevention use cases.

Deep LearningKM curveKwaiSurvival

0 likes · 15 min read

Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework

JD Cloud Developers

Dec 21, 2021 · Artificial Intelligence

How JD Cloud’s Mobile Super‑Resolution SDK Boosts Video Quality and Cuts Bandwidth by 30%

JD Cloud’s new mobile super‑resolution SDK leverages deep‑learning ESPCN algorithms with ROI‑based processing to upscale video streams in real time, delivering up to 80% longer playback, 30% lower bandwidth costs, and measurable quality gains demonstrated through PSNR, VMAF, and SSIM metrics.

Bandwidth ReductionDeep LearningESPCN

0 likes · 6 min read

How JD Cloud’s Mobile Super‑Resolution SDK Boosts Video Quality and Cuts Bandwidth by 30%

DataFunTalk

Dec 18, 2021 · Artificial Intelligence

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

This work proposes an adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and heterogeneous group behaviors to predict fine‑grained urban travel demand, demonstrating over 10% performance gains on real‑world Beijing and Shanghai datasets compared with classic baselines.

Deep LearningGraph Neural NetworkTraffic Prediction

0 likes · 24 min read

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

ITPUB

Dec 13, 2021 · Artificial Intelligence

How Data Augmentation Boosts Machine Learning When Data Is Scarce

This article explains how data augmentation can alleviate overfitting by artificially expanding limited training sets, outlines common transformation techniques for images, text, and audio, and discusses the method's benefits, practical applications, and inherent limitations for machine‑learning practitioners.

Deep Learningcomputer visiondata augmentation

0 likes · 6 min read

How Data Augmentation Boosts Machine Learning When Data Is Scarce

DataFunTalk

Dec 13, 2021 · Artificial Intelligence

Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval

The article introduces the Dual Vector Foil (DVF) algorithm system, which decouples index construction from model training to enable lightweight, high‑precision large‑scale recall using arbitrary complex models, and details its two‑stage and one‑stage solutions, graph‑based retrieval implementation, performance optimizations, and experimental results.

Deep LearningRecommendation Systemsalgorithm

0 likes · 28 min read

Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval

Kuaishou Tech

Dec 13, 2021 · Artificial Intelligence

AI-Powered High-Resolution Portrait Restoration Using StyleGAN and Face Parsing

This article describes an AI-driven portrait enhancement system that restores degraded facial images by simulating degradation, constructing paired datasets, and employing a StyleGAN‑based generator combined with face‑parsing masks, detailing the pipeline, model architecture, training losses, and achieved high‑quality results.

AIDeep LearningStyleGAN

0 likes · 9 min read

AI-Powered High-Resolution Portrait Restoration Using StyleGAN and Face Parsing

Code DAO

Dec 12, 2021 · Artificial Intelligence

Lightning Flash 0.3 Introduces New Tasks, Visualization Tools, Data Pipelines, and Registry API

Lightning Flash 0.3 expands the PyTorch Lightning ecosystem with eight new computer‑vision and NLP tasks, modular API design, integrated model hubs, visualisation callbacks, customizable data‑source hooks, and a central registry for model backbones, all illustrated with concrete code examples.

Deep LearningLightning FlashPyTorch Lightning

0 likes · 7 min read

Lightning Flash 0.3 Introduces New Tasks, Visualization Tools, Data Pipelines, and Registry API

Code DAO

Dec 11, 2021 · Artificial Intelligence

Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance

The article analyzes how Nimble reduces GPU scheduling overhead and enables parallel execution through ahead‑of‑time scheduling and automatic multi‑stream assignment, achieving up to 22.3× inference speedup over PyTorch and significantly improving GPU utilization for deep learning workloads.

Deep LearningGPU schedulingParallel Execution

0 likes · 9 min read

Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance