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1276 articles · Page 10 of 13
DataFunTalk
DataFunTalk
Mar 18, 2020 · Artificial Intelligence

Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu

This article presents a comprehensive technical overview of Meitu's personalized push notification pipeline, detailing the evolution of embedding methods (Word2Vec, Airbnb listing embedding, graph embedding), multiple recall strategies (global, personalized, attribute, and content‑based), and a progression of ranking models from logistic regression to field‑wise three‑tower architectures, highlighting their impact on click‑through rates.

AIDeep LearningPush Notification
0 likes · 12 min read
Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu
DataFunTalk
DataFunTalk
Mar 16, 2020 · Artificial Intelligence

Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering

This article presents a comprehensive overview of Phoenix News's AI‑driven feed recommendation system, detailing its business challenges, multi‑stage architecture, deep learning models, feature pipelines, metric trade‑offs, cold‑start solutions, and practical insights for improving user satisfaction and content quality.

AIDeep Learningfeature engineering
0 likes · 22 min read
Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 13, 2020 · Artificial Intelligence

How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations

The article presents the Deep Match to Rank (DMR) model, which integrates collaborative‑filtering inspired user‑to‑item relevance modeling into the ranking stage of recommendation systems, achieving significant offline and online improvements in click‑through rate and revenue metrics for e‑commerce platforms.

CTR predictionDeep LearningE‑commerce
0 likes · 11 min read
How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations
DataFunTalk
DataFunTalk
Mar 12, 2020 · Artificial Intelligence

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

This article describes the end‑to‑end recommendation pipeline of the Province Money Fast Report app, covering business background, data collection, model training and evaluation, the evolution from FM to DeepFM, DIN, DCN, xDeepFM, ESMM and custom networks, as well as serving strategies and practical lessons learned.

CTR predictionDeep Learningfeature engineering
0 likes · 28 min read
Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App
21CTO
21CTO
Mar 11, 2020 · Artificial Intelligence

Understanding the AI Revolution: From Basics to Future Impact

This article explains the rapid rise of artificial intelligence, outlines China's national AI development plan, compares human and machine intelligence, discusses current AI applications, future job impacts, and the infrastructure that will power the next industrial revolution.

AI ApplicationsArtificial IntelligenceDeep Learning
0 likes · 10 min read
Understanding the AI Revolution: From Basics to Future Impact
58 Tech
58 Tech
Mar 11, 2020 · Artificial Intelligence

qa_match: An Open‑Source Deep Learning Based Question‑Answer Matching System

The article introduces qa_match, an open‑source lightweight QA matching tool built on TensorFlow that combines BiLSTM‑based domain classification, DSSM‑based intent matching, and a model‑fusion strategy to deliver accurate, multi‑type responses for intelligent customer service applications.

AIBiLSTMDSSM
0 likes · 12 min read
qa_match: An Open‑Source Deep Learning Based Question‑Answer Matching System
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 10, 2020 · Artificial Intelligence

Can Frequency‑Domain Learning Boost Image Inference Efficiency?

This article presents a system‑level approach that performs deep‑learning inference directly on JPEG frequency components, uses a gating mechanism to select important DCT coefficients, and demonstrates higher accuracy with far lower bandwidth for image classification and instance segmentation tasks.

Bandwidth ReductionDeep Learningcomputer vision
0 likes · 22 min read
Can Frequency‑Domain Learning Boost Image Inference Efficiency?
JD Tech Talk
JD Tech Talk
Mar 9, 2020 · Artificial Intelligence

Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits

The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.

Deep LearningKnowledge GraphMultimodal AI
0 likes · 6 min read
Advances in Deep Learning for Content Recommendation and User Behavior Modeling by JD Digits
Qunar Tech Salon
Qunar Tech Salon
Mar 4, 2020 · Artificial Intelligence

Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction

The paper proposes the Deep Match to Rank (DMR) model, which integrates matching‑stage collaborative‑filtering ideas into the ranking stage to explicitly represent user‑to‑item relevance, thereby enhancing personalization and achieving significant CTR and DPV improvements in e‑commerce recommendation scenarios.

CTR predictionDeep LearningRecommendation Systems
0 likes · 12 min read
Deep Match to Rank (DMR) Model for Personalized Click‑Through Rate Prediction
TAL Education Technology
TAL Education Technology
Feb 28, 2020 · Artificial Intelligence

TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models

This article describes the TPNN deep‑learning platform’s multi‑GPU acceleration, data‑parallel BMUF training, LSTM‑CTC acoustic modeling, and a suite of mobile‑side optimizations—including model pruning, 8‑bit quantization, low‑precision matrix multiplication and mixed‑precision computation—that together achieve over 92% recognition accuracy for children’s English speech on both server and mobile devices.

BMUFCTCDeep Learning
0 likes · 15 min read
TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models
Tencent Advertising Technology
Tencent Advertising Technology
Feb 28, 2020 · Artificial Intelligence

Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems

This article presents a Bayesian smoothing approach to alleviate cold-start problems in click-through rate estimation, introduces key-value memory networks to incorporate prior knowledge, and proposes methods to convert continuous features into dictionary embeddings for deep learning models in recommendation systems.

Deep Learningclick-through ratecontinuous feature embedding
0 likes · 18 min read
Bayesian Smoothing and Key-Value Memory Networks for Click-Through Rate Prediction in Recommendation Systems
Tencent Tech
Tencent Tech
Feb 27, 2020 · Artificial Intelligence

How to Speed Up Deep Learning Models: Cutting-Edge Acceleration Techniques

Deep learning models often suffer from slow training and deployment due to their size, but a range of advanced acceleration methods—including model architecture optimization, pruning, quantization, knowledge distillation, and distributed training techniques—can dramatically improve speed and efficiency while maintaining performance.

Deep LearningDistributed TrainingPruning
0 likes · 14 min read
How to Speed Up Deep Learning Models: Cutting-Edge Acceleration Techniques
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 25, 2020 · Artificial Intelligence

How Attribute‑Specific Embedding Networks Revolutionize Fashion Copyright Protection

A new AI algorithm jointly developed by Alibaba Security and Zhejiang University learns fine‑grained, attribute‑aware similarity embeddings for fashion images, enabling accurate detection of local design plagiarism and improving retrieval performance across multiple benchmark datasets.

Deep Learningattribute embeddingcomputer vision
0 likes · 14 min read
How Attribute‑Specific Embedding Networks Revolutionize Fashion Copyright Protection
DataFunTalk
DataFunTalk
Feb 22, 2020 · Artificial Intelligence

Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI

The article introduces iQIYI's double‑DNN ranking architecture that combines a high‑performance teacher network with a lightweight student network through online knowledge distillation, detailing the evolution of deep learning‑based ranking models, the motivation for model upgrades, training pipelines, and experimental results that demonstrate significant latency reduction and ROI improvement.

Deep LearningRanking ModelsRecommendation Systems
0 likes · 13 min read
Double DNN Ranking Model with Online Knowledge Distillation for Real‑Time Recommendation at iQIYI
DataFunTalk
DataFunTalk
Feb 20, 2020 · Artificial Intelligence

Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

This article reviews perception technologies used in autonomous heavy‑truck systems—including lane‑line detection, obstacle detection, and LiDAR sensing—detailing traditional and deep‑learning approaches, practical challenges on high‑speed highways, and the cost, performance, and reliability issues faced when moving these solutions to mass production.

Autonomous DrivingDeep LearningLiDAR
0 likes · 16 min read
Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations
DataFunTalk
DataFunTalk
Feb 13, 2020 · Artificial Intelligence

Deep Learning Techniques and Challenges in Autonomous Driving

This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.

Autonomous DrivingDeep LearningEnd-to-End
0 likes · 8 min read
Deep Learning Techniques and Challenges in Autonomous Driving
DataFunTalk
DataFunTalk
Feb 10, 2020 · Artificial Intelligence

Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)

The article describes Ctrip's Prophet platform, which combines Flink real‑time stream processing with TensorFlow deep‑learning models to provide intelligent, low‑latency anomaly detection, replacing traditional rule‑based alerts and addressing challenges such as holiday traffic and model scalability.

AIDeep LearningFlink
0 likes · 13 min read
Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)
Python Programming Learning Circle
Python Programming Learning Circle
Feb 8, 2020 · Artificial Intelligence

Nine Recommended Programming Books for Home Learning

During the stay‑at‑home period, this article suggests nine concise programming books covering Python neural networks, web crawling, deep learning with PyTorch, machine learning fundamentals, zero‑trust network security, classic programming pearls, Python mathematics, AI algorithms, and Vim text processing, each with brief descriptions and images.

AIDeep LearningVim
0 likes · 9 min read
Nine Recommended Programming Books for Home Learning
Python Programming Learning Circle
Python Programming Learning Circle
Feb 8, 2020 · Artificial Intelligence

Neural Network Construction Example with Python Implementation

This article presents a comprehensive tutorial on building and training a multi‑layer neural network in Python, covering data preprocessing, model architecture definition, parameter initialization, forward and backward propagation, cost computation, and parameter updates with code examples for activation functions and optimization techniques.

Deep Learning
0 likes · 13 min read
Neural Network Construction Example with Python Implementation
Meituan Technology Team
Meituan Technology Team
Feb 6, 2020 · Artificial Intelligence

Building a One-Stop Machine Learning Platform: Meituan's Turing Platform

Meituan’s Turing platform consolidates the entire delivery‑order workflow—from massive data ingestion and feature generation to model training, evaluation, deployment, real‑time prediction, and AB testing—into a single, end‑to‑end system that evolved from a minimal MVP into a fully platformized solution, addressing speed, accuracy, and engineering‑algorithm decoupling while planning deeper deep‑learning integration.

AB testingDeep LearningMachine Learning Platform
0 likes · 16 min read
Building a One-Stop Machine Learning Platform: Meituan's Turing Platform
DataFunTalk
DataFunTalk
Feb 3, 2020 · Artificial Intelligence

Advances in Speech Recognition: Concepts, Deep Learning Methods, and Didi’s Applications

This article presents a comprehensive overview of modern speech recognition technology, covering basic ASR concepts, classic acoustic and language models, deep‑learning approaches such as DNN‑HMM, CTC, attention‑based and transformer models, multimodal fusion, signal‑processing pipelines, and practical deployment considerations at Didi.

ASRCTCDeep Learning
0 likes · 15 min read
Advances in Speech Recognition: Concepts, Deep Learning Methods, and Didi’s Applications
Huajiao Technology
Huajiao Technology
Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

This article reviews the evolution of ranking models in modern recommendation systems, covering traditional linear models, factorization machines, tree‑based GBDT+LR, and a range of deep learning architectures such as Wide&Deep, DeepFM, DCN, xDeepFM, DIN, as well as multi‑task frameworks like ESMM and MMOE, and finally illustrates their practical deployment in a live streaming platform.

Deep LearningMachine LearningRecommendation Systems
0 likes · 20 min read
Overview of Ranking Algorithms in Recommendation Systems
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 17, 2020 · Artificial Intelligence

Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform

This article describes how a BERT‑based model is fine‑tuned to compute sentence‑pair similarity for improving recommendation accuracy in an online school, detailing the architecture, training mechanisms, code implementation, experimental results, and future extensions such as sentiment analysis.

BERTChinese NLPDeep Learning
0 likes · 20 min read
Fine‑tuning BERT for Sentence Pair Similarity in an Online Education Platform
DataFunTalk
DataFunTalk
Jan 16, 2020 · Artificial Intelligence

Voice Conversion: Fundamentals, Methods, and iQIYI Applications

This article provides a comprehensive overview of voice conversion technology, covering its definition, parallel and non‑parallel data approaches, classic and deep‑learning methods such as DTW, GMM, seq2seq, PPG, VAE, Flow, GAN, and practical applications and challenges in iQIYI’s products.

ASRDeep LearningGaN
0 likes · 8 min read
Voice Conversion: Fundamentals, Methods, and iQIYI Applications
Tencent Cloud Developer
Tencent Cloud Developer
Jan 14, 2020 · Artificial Intelligence

MedicalNet: Tencent's Pre-trained Model for 3D Medical Imaging AI

MedicalNet, Tencent’s open-source framework, aggregates diverse small 3D medical imaging datasets into a large pre-training corpus, applies dataset filtering and joint spatial-pixel normalization, and provides encoder-decoder models that accelerate convergence and boost accuracy for AI-driven diagnosis in data-scarce medical imaging scenarios.

3D Medical ImagingDeep LearningHealthcare AI
0 likes · 5 min read
MedicalNet: Tencent's Pre-trained Model for 3D Medical Imaging AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 10, 2020 · Artificial Intelligence

How AI Powers Ground Marker Recognition for High‑Precision Maps

This article details the evolution of ground‑marker recognition technology in high‑precision maps, covering challenges of diverse and worn markings, traditional segmentation methods, deep‑learning breakthroughs such as R‑FCN, cascade detectors, corner‑point detection, semantic segmentation, PAnet, and 3‑D point‑cloud approaches, and their impact on accuracy and production efficiency.

Deep Learningcomputer visionground marker recognition
0 likes · 17 min read
How AI Powers Ground Marker Recognition for High‑Precision Maps
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 9, 2020 · Artificial Intelligence

Results and Winning Solutions of the 2019 CCF Big Data & Computing Intelligence Contest – Video Copyright Detection Track

The 2019 CCF Big Data & Computing Intelligence Contest’s Video Copyright Detection track, judged by iQIYI, saw 705 teams from 25 countries compete, with Hengyang Data’s VGG‑16‑based solution winning, followed by Boyun Vision, Xiao Jia’s Lao Liang, Hulu Brothers and Beihang University, showcasing diverse deep‑learning and unsupervised approaches for robust video copyright detection.

Artificial IntelligenceCCF ContestDeep Learning
0 likes · 9 min read
Results and Winning Solutions of the 2019 CCF Big Data & Computing Intelligence Contest – Video Copyright Detection Track
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 9, 2020 · Artificial Intelligence

Voice Conversion (VC): Fundamentals, Progress, and Applications

Voice conversion (VC) technology changes a speaker’s timbre and style while keeping the spoken text unchanged, supporting one‑to‑one, many‑to‑one, and many‑to‑many scenarios for medical assistance and entertainment, using parallel or non‑parallel data through methods such as DTW‑aligned frame mapping, attention‑based neural networks, PPG‑LSTM pipelines, VAEs, normalizing‑flow models, and GANs, with iQIYI focusing on non‑parallel data, prosody preservation, and noise‑robust augmentation.

Artificial IntelligenceDeep LearningGaN
0 likes · 12 min read
Voice Conversion (VC): Fundamentals, Progress, and Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 3, 2020 · Artificial Intelligence

How Alibaba’s DAMO Lab Revolutionizes Image Cutout with AI‑Powered Matting

Alibaba's DAMO Academy details its AI‑driven image cutout system, describing why automated matting is needed, the four‑module pipeline (filtering, classification, detection, segmentation), architectural innovations such as dual decoders and fusion networks, and how these advances enable product‑level batch background removal.

AIAlibabaDeep Learning
0 likes · 9 min read
How Alibaba’s DAMO Lab Revolutionizes Image Cutout with AI‑Powered Matting
DataFunTalk
DataFunTalk
Dec 24, 2019 · Artificial Intelligence

Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval

This article surveys the development of recall modules in large‑scale recommendation systems, covering traditional item‑based collaborative filtering, single‑embedding DNN and dual‑tower approaches, multi‑interest capsule networks, graph‑based embeddings, long‑short term interest modeling, and the tree‑structured TDM framework for efficient deep matching.

Deep LearningRecommendation Systemsgraph embedding
0 likes · 14 min read
Evolution of Recall Models in Recommendation Systems: From Collaborative Filtering to Deep Learning and Tree‑Based Retrieval
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 20, 2019 · Artificial Intelligence

Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model

The iQIYI advertising team introduced an LSTM‑based deep‑learning model that forecasts inventory by normalizing data, clustering dimensions, and embedding fine‑grained holiday features, achieving significantly lower bias than their Adaptive‑ARIMA baseline and improving generalization while reducing training resources.

Advertising ForecastingDeep LearningLSTM
0 likes · 10 min read
Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model
360 Quality & Efficiency
360 Quality & Efficiency
Dec 20, 2019 · Artificial Intelligence

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

This article describes a complete pipeline for recommending automated test scripts for APK releases, covering CSV data preprocessing, feature encoding, tokenization with pkuseg and jieba, and training various machine‑learning models such as LDA, word2vec, XGBoost, deep neural networks, and multi‑label classifiers to predict script execution order.

APK testingDeep LearningMachine Learning
0 likes · 14 min read
Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline
DataFunTalk
DataFunTalk
Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNDeep LearningRNN
0 likes · 36 min read
A Comprehensive Overview of Sequential Recommendation Models and Techniques
Amap Tech
Amap Tech
Dec 13, 2019 · Artificial Intelligence

Image Segmentation for High-Definition Mapping: Evolution and Practices at Gaode Maps

Gaode Maps has progressed image segmentation from early heuristic region splitting to modern deep‑learning pipelines—leveraging FCNs, multi‑task networks, Mask R‑CNN, and specialized losses—to achieve centimeter‑level, instance‑aware mapping of roads, signs, and small objects while pursuing lighter, real‑time models.

AIDeep LearningGaode Maps
0 likes · 14 min read
Image Segmentation for High-Definition Mapping: Evolution and Practices at Gaode Maps
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
UCloud Tech
UCloud Tech
Dec 10, 2019 · Artificial Intelligence

Train and Deploy a CIFAR‑10 Image Classification Model with UAI Platform

This tutorial walks university students through the complete workflow of using the CIFAR‑10 dataset to train a convolutional neural network for image classification and then deploying the model as an online inference service on the UAI‑Train and UAI‑Inference platforms.

CIFAR-10Deep LearningDocker
0 likes · 6 min read
Train and Deploy a CIFAR‑10 Image Classification Model with UAI Platform
DataFunTalk
DataFunTalk
Dec 10, 2019 · Artificial Intelligence

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

This article details a series of reinforcement‑learning experiments on the 2048 game, from random baselines through DQN implementations, classical value‑iteration methods, network redesigns, and Monte‑Carlo tree search, highlighting challenges such as reward design, over‑estimation, and exploration while achieving scores up to 34 000 and tiles of 2048.

2048AIDQN
0 likes · 8 min read
Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights
DataFunTalk
DataFunTalk
Dec 4, 2019 · Artificial Intelligence

Joint Optimization of Tree‑based Index and Deep Model (JTM) for Large‑Scale Recommendation

This article presents JTM, a joint optimization framework that simultaneously learns a tree‑based index and a deep scoring model to overcome the limitations of traditional recommendation pipelines, demonstrating significant recall improvements on Amazon Books and Alibaba UserBehavior datasets through hierarchical user interest modeling and efficient tree learning.

Deep Learningjoint optimizationlarge-scale
0 likes · 19 min read
Joint Optimization of Tree‑based Index and Deep Model (JTM) for Large‑Scale Recommendation
DataFunTalk
DataFunTalk
Nov 26, 2019 · Artificial Intelligence

Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

This article surveys two neural news recommendation approaches—NAML, which uses multi‑view learning to fuse heterogeneous news information, and NPA, which incorporates personalized attention for both words and news items—demonstrating their superior performance over strong baselines on real‑world MSN news data through extensive experiments and visual analyses.

AIDeep LearningMulti-view learning
0 likes · 11 min read
Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 22, 2019 · Artificial Intelligence

Analysis of ICCV 2019 Lightweight Face Recognition Challenge Champion Solutions

The ICCV 2019 Lightweight Face Recognition Challenge attracted 292 teams and defined four strict FLOP‑ and size‑limited protocols for image and video recognition, with champions employing near‑30 GFLOP EfficientNet‑style backbones, novel loss functions, frame‑fusion, and knowledge‑distilled VarGNet models to balance accuracy and computational budget.

Deep LearningICCV ChallengeLightweight Face Recognition
0 likes · 8 min read
Analysis of ICCV 2019 Lightweight Face Recognition Challenge Champion Solutions
Meituan Technology Team
Meituan Technology Team
Nov 21, 2019 · Artificial Intelligence

StarNet: Global Interaction Network for Pedestrian Trajectory Prediction

StarNet is a neural network for pedestrian trajectory prediction in large‑scale delivery, using a global dynamic map and a Hub‑Host architecture to model interactions efficiently, reducing complexity from O(N²) to O(N), and achieving higher accuracy with fast inference compared to baseline methods.

Deep LearningLSTMStarNet
0 likes · 15 min read
StarNet: Global Interaction Network for Pedestrian Trajectory Prediction
Ctrip Technology
Ctrip Technology
Nov 21, 2019 · Artificial Intelligence

Designing and Deploying an NLP Model for Airline Ticket Customer Service

This article describes the end‑to‑end development of a multi‑class NLP system for Ctrip airline ticket customer service, covering problem analysis, data preprocessing, sample balancing, model architecture (TextCNN and Bi‑GRU), training strategies, performance evaluation, and online customization to achieve high accuracy in intent recognition.

Bi-GRUCustomer ServiceDeep Learning
0 likes · 16 min read
Designing and Deploying an NLP Model for Airline Ticket Customer Service
MaGe Linux Operations
MaGe Linux Operations
Nov 20, 2019 · Artificial Intelligence

How North Korea Built a Homegrown AI Facial‑Recognition Smartphone

North Korea’s newly unveiled “Blue Sky” smartphone incorporates a homegrown AI facial‑recognition system built on CNNs, MTCNN, MobileFaceNets and TensorFlow, showcasing how the isolated nation is advancing edge AI despite operating solely on its internal CentOS‑based intranet.

AIDeep LearningNorth Korea
0 likes · 7 min read
How North Korea Built a Homegrown AI Facial‑Recognition Smartphone
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 15, 2019 · Artificial Intelligence

Boosting Online Shopping with AI-Powered 3D Scene Merchandising

This article explores how Alibaba’s 3D scene‑based recommendation system combines computer‑vision, deep‑learning and data‑driven matching algorithms to create immersive, size‑accurate product visualizations that enhance user experience and drive higher click‑through rates in e‑commerce.

3d-visualizationDeep LearningE‑commerce
0 likes · 12 min read
Boosting Online Shopping with AI-Powered 3D Scene Merchandising
Amap Tech
Amap Tech
Nov 14, 2019 · Artificial Intelligence

Technical Evolution of Ground Marking Recognition for High‑Precision Maps

AMap’s ground‑marking recognition has progressed from simple threshold methods to advanced deep‑learning pipelines—including two‑stage R‑FCN, cascade detectors with local regression, corner‑point and segmentation hybrids, and LiDAR‑based 3‑D PointRCNN—achieving over 99 % recall and sub‑5 cm positional accuracy for high‑precision map production.

Deep Learningcomputer visionground marking
0 likes · 15 min read
Technical Evolution of Ground Marking Recognition for High‑Precision Maps
JD Retail Technology
JD Retail Technology
Nov 6, 2019 · Artificial Intelligence

Technical Overview of JD.com Search and Recommendation Systems for the 11.11 Shopping Festival

The article details JD.com's internally developed distributed search engine and recommendation platform, their new architectures, deep‑learning‑driven ranking and recall models, component‑based deployment, extensive performance testing, and coordinated operations that powered the massive 11.11 shopping event.

Deep Learninge‑commerceoperations
0 likes · 5 min read
Technical Overview of JD.com Search and Recommendation Systems for the 11.11 Shopping Festival
Baidu App Technology
Baidu App Technology
Oct 30, 2019 · Artificial Intelligence

Applying Deep Learning and AI on Mobile: Baidu App Cases and Technical Insights

The Baidu App team showcases how deep‑learning and AI can be deployed on mobile through on‑device and server‑side inference—illustrated by plant‑identification, stylized filters, video subject detection, and AR real‑time translation—while addressing model compression, cross‑platform optimization, and offering a practical guide for engineers.

AR TranslationDeep Learningcomputer vision
0 likes · 11 min read
Applying Deep Learning and AI on Mobile: Baidu App Cases and Technical Insights
DataFunTalk
DataFunTalk
Oct 24, 2019 · Artificial Intelligence

Evolution and Engineering Practices of the 360 Display Advertising Recall System

This article details the 360 display advertising system's architecture and the progressive evolution of its recall module, covering business overview, overall pipeline, various recall strategies—including Boolean, vectorized, and deep‑tree approaches—and the performance optimizations applied to meet real‑time constraints.

AdvertisingDeep Learningrecall system
0 likes · 14 min read
Evolution and Engineering Practices of the 360 Display Advertising Recall System
Amap Tech
Amap Tech
Oct 23, 2019 · Artificial Intelligence

AR Navigation Lane Detection: Methods, Challenges, and Practical Solutions

The article reviews AR navigation lane‑detection, comparing traditional handcrafted visual pipelines with modern deep‑learning segmentation approaches, proposes an efficient multitask network with weight‑allocation and vanishing‑point anchoring, and demonstrates quantized models achieving real‑time, stable performance on low‑power automotive chips while outlining remaining weather, lighting, and road‑condition challenges.

ADASAR navigationDeep Learning
0 likes · 16 min read
AR Navigation Lane Detection: Methods, Challenges, and Practical Solutions
Tencent Advertising Technology
Tencent Advertising Technology
Oct 17, 2019 · Artificial Intelligence

Visual Algorithm Applications in Advertising Scenarios

The talk outlines how Tencent Advertising leverages deep‑learning visual algorithms—including GCN‑based edge refinement, template generation, AutoML‑driven smart review, and a dual‑tower click‑through‑rate model—to automate creative production, improve ad quality, and enhance user experience across creation, review, and playback stages.

AIAdvertisingAutoML
0 likes · 7 min read
Visual Algorithm Applications in Advertising Scenarios
DataFunTalk
DataFunTalk
Oct 16, 2019 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking

This article details Meitu's large‑scale personalized recommendation pipeline, describing the business scenario, challenges of massive data, latency and long‑tail distribution, and the application of deep learning techniques such as Item2vec, YouTubeNet, dual‑tower DNN, NFM, NFwFM and multi‑task learning to improve click‑through rate, conversion and user engagement.

Deep LearningRecommendation Systemslarge-scale
0 likes · 20 min read
Deep Learning Practices for Personalized Recommendation at Meitu: From Recall to Ranking
DataFunTalk
DataFunTalk
Oct 14, 2019 · Artificial Intelligence

Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements

This article presents a comprehensive overview of short‑video recommendation at UC, covering business background, system architecture, the evolution from LR to Wide & Deep models, multi‑objective loss design with positive‑sample weighting, graph‑embedding fusion, time‑weighted loss, continuity modeling, a Boosting‑based WnD solution, and future research directions.

Deep Learningboostinggraph embedding
0 likes · 11 min read
Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements
58 Tech
58 Tech
Oct 12, 2019 · Artificial Intelligence

Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering

This article details 58.com’s recruitment recommendation platform, describing its personalized matching challenges, typical recommendation scenarios, a three‑stage ranking framework, optimization goals, the evolution from rule‑based methods to logistic regression, factorization machines, XGBoost, and deep learning models, extensive feature engineering practices, and future research directions.

AIDeep LearningMachine Learning
0 likes · 16 min read
Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering
Meituan Technology Team
Meituan Technology Team
Oct 10, 2019 · Artificial Intelligence

Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning

The paper chronicles Meituan’s three‑stage evolution of delivery‑time estimation—from a hierarchical address tree with local linear regression, through a vector‑retrieval system that boosts recall, to a lightweight end‑to‑end deep‑learning model that meets sub‑5 ms latency while delivering progressively lower error and full coverage.

Deep LearningMachine Learningdelivery time estimation
0 likes · 21 min read
Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 10, 2019 · Artificial Intelligence

How Joint Optimization of Tree-Based Indexes Boosts Large-Scale Recommendation Accuracy

This article introduces JTM, a joint optimization framework that simultaneously learns deep scoring models and tree-structured indexes, addressing the limitations of traditional recommendation pipelines and demonstrating significant precision and recall gains on large-scale datasets such as Amazon Books and UserBehavior.

Deep LearningLarge-Scale RetrievalRecommendation Systems
0 likes · 20 min read
How Joint Optimization of Tree-Based Indexes Boosts Large-Scale Recommendation Accuracy
DataFunTalk
DataFunTalk
Sep 29, 2019 · Artificial Intelligence

UC Information Flow Video Tag Recognition: System Architecture and Multi‑Modal Algorithms

This article presents a comprehensive overview of UC's information‑flow video tag recognition technology, detailing tag usage scenarios, the end‑to‑end system architecture, multi‑modal feature extraction, advanced deep‑learning models such as NextVlad, behavior and person tagging methods, and future research directions.

Deep LearningRecommendation Systemscomputer vision
0 likes · 14 min read
UC Information Flow Video Tag Recognition: System Architecture and Multi‑Modal Algorithms
DataFunTalk
DataFunTalk
Sep 27, 2019 · Artificial Intelligence

Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models

The talk by Meitu senior algorithm expert Chen Wenqiang details how deep‑learning‑driven embedding, recall, and ranking techniques—including Item2vec, twin‑tower DNNs, and multi‑task NFwFM—are applied to improve click‑through rates, follow conversions, and user engagement in Meitu's content community.

AIDeep LearningRecommendation Systems
0 likes · 3 min read
Applying Deep Learning to Meitu Community Recommendation: Embedding, Recall, and Ranking Models
Meituan Technology Team
Meituan Technology Team
Sep 26, 2019 · Artificial Intelligence

Efficient Scene Text Detection Framework with Feature Pyramid and Expanded High-Level Feature Maps

The paper presents an efficient scene‑text detector that expands high‑level SSD feature maps and integrates a feature‑pyramid network, using direction‑aware segment‑and‑link predictions to reconstruct arbitrarily long, rotated text, achieving higher recall and precision with real‑time speed and outperforming recent methods on ICDAR benchmarks and a menu‑recognition test.

Deep LearningICDARSSD
0 likes · 12 min read
Efficient Scene Text Detection Framework with Feature Pyramid and Expanded High-Level Feature Maps
UCloud Tech
UCloud Tech
Sep 24, 2019 · Artificial Intelligence

Cut GPU Costs by 75%: AI‑Driven Car Fault Detection with UCloud Hot‑Standby

The article explains how the WeiChe app leverages AI to instantly recognize car dashboard warning lights, describes the underlying deep‑learning infrastructure on UCloud’s UAI‑Inference platform, and shows how the Hot‑Standby feature dramatically cuts GPU costs while maintaining real‑time performance.

AICloud InferenceDeep Learning
0 likes · 8 min read
Cut GPU Costs by 75%: AI‑Driven Car Fault Detection with UCloud Hot‑Standby
Xianyu Technology
Xianyu Technology
Sep 12, 2019 · Artificial Intelligence

Deep Learning for Automated Module Detection in Taobao 99 Promotion Pages

This study presents a deep‑learning pipeline that employs a Cascade‑RCNN with Feature Pyramid Network to automatically detect and refine modules and their internal elements on Taobao’s 99‑promotion pages, achieving roughly 98 % precision and recall on a thousand‑image validation set and paving the way for broader e‑commerce event applications.

Cascade R-CNNDeep LearningTaobao
0 likes · 7 min read
Deep Learning for Automated Module Detection in Taobao 99 Promotion Pages
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 12, 2019 · Artificial Intelligence

How a Simple Learning‑Rate Trick Detects 90% of Noisy Labels in Image Data

Training deep neural networks on large‑scale weakly labeled image data suffers from noisy annotations that degrade performance, but a simple algorithm that adjusts the learning‑rate during training can automatically identify up to 90% of noisy samples, improving dataset cleanliness and model accuracy without manual intervention.

Deep Learningdata cleaningimage classification
0 likes · 15 min read
How a Simple Learning‑Rate Trick Detects 90% of Noisy Labels in Image Data
Qunar Tech Salon
Qunar Tech Salon
Sep 12, 2019 · Artificial Intelligence

A Comprehensive Overview of Attention Mechanisms in Deep Learning

This article systematically reviews the history, core concepts, variants, and practical implementations of attention mechanisms—from early additive and multiplicative forms to self‑attention, multi‑head attention, and recent transformer‑based models—highlighting why attention has become fundamental in modern AI research.

Deep LearningNLPSelf-Attention
0 likes · 16 min read
A Comprehensive Overview of Attention Mechanisms in Deep Learning
DataFunTalk
DataFunTalk
Sep 5, 2019 · Artificial Intelligence

Baidu Semantic Computing: ERNIE, SimNet, and Future Directions in Natural Language Processing

This article reviews Baidu's research on semantic computing, covering the evolution of semantic representation, the development and evaluation of the ERNIE and SimNet models, their industrial applications, model compression techniques, and outlines future research priorities in multilingual and multimodal semantic understanding.

Deep LearningERNIENatural Language Processing
0 likes · 12 min read
Baidu Semantic Computing: ERNIE, SimNet, and Future Directions in Natural Language Processing
HomeTech
HomeTech
Sep 4, 2019 · Artificial Intelligence

Accelerating TensorFlow Model Inference with NVIDIA TensorRT: Methods, Experiments, and Results

This article explains how to use NVIDIA TensorRT to accelerate TensorFlow model inference by describing TensorRT architecture, optimization techniques such as layer fusion and precision calibration, detailing the conversion of frozen_graph and saved_model formats, presenting experimental setup and performance comparisons, and summarizing the achieved speed‑up.

Deep LearningInference AccelerationModel Optimization
0 likes · 13 min read
Accelerating TensorFlow Model Inference with NVIDIA TensorRT: Methods, Experiments, and Results
Liangxu Linux
Liangxu Linux
Sep 3, 2019 · Artificial Intelligence

Clone Any Voice in Seconds with the Real-Time-Voice-Cloning Open‑Source TTS

This guide explains how the Real-Time-Voice-Cloning project uses deep‑learning text‑to‑speech techniques to generate a voice clone from a short audio sample, covering the underlying principle, required dataset, setup steps, demo usage, and ethical considerations.

Deep LearningReal-Time-Voice-CloningText-to-Speech
0 likes · 5 min read
Clone Any Voice in Seconds with the Real-Time-Voice-Cloning Open‑Source TTS
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 3, 2019 · Artificial Intelligence

Unlocking Scalable Private‑Domain Recommendations with a “4+N” Architecture

This article describes a systematic, standardized, and automated “4+N” recommendation framework that unifies features, samples, models, and pipelines to accelerate private‑domain marketing recommendations across multiple scenarios while improving accuracy, efficiency, and business impact.

AI architectureDeep LearningModel deployment
0 likes · 12 min read
Unlocking Scalable Private‑Domain Recommendations with a “4+N” Architecture
360 Tech Engineering
360 Tech Engineering
Aug 28, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP

This article explains how Huajiao Live leverages Spark for data preprocessing and TensorFlow (augmented by the TensorSlow project) for distributed deep‑learning training, detailing computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and a step‑by‑step Python implementation of a multi‑layer perceptron.

Computational GraphDeep LearningMLP
0 likes · 14 min read
Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 27, 2019 · Artificial Intelligence

How Transformers Enable Personalized Outfit Generation for Fashion Recommendation

This article presents a Transformer‑based framework that simultaneously generates visually compatible outfits and personalizes recommendations by leveraging multimodal item embeddings and user behavior, achieving significant gains in compatibility prediction, fill‑in‑the‑blank accuracy, and click‑through rate on Alibaba's iFashion platform.

Deep LearningTransformere‑commerce
0 likes · 15 min read
How Transformers Enable Personalized Outfit Generation for Fashion Recommendation
Huajiao Technology
Huajiao Technology
Aug 27, 2019 · Artificial Intelligence

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

This article provides a comprehensive technical overview of collaborative filtering, covering traditional user‑ and item‑based similarity methods, matrix‑factorization approaches for implicit feedback, various loss functions, and a suite of deep neural network models such as GMF, MLP, NeuMF, DMF, and ConvMF, together with implementation details, evaluation metrics, and practical deployment considerations.

Collaborative FilteringDeep LearningRecommendation Systems
0 likes · 29 min read
Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models
Beike Product & Technology
Beike Product & Technology
Aug 23, 2019 · Artificial Intelligence

Deep Learning from Theory to Practice: Neural Networks, Logistic Regression, TensorFlow and Keras for Cat Image Classification

This tutorial walks readers through the fundamentals of artificial neural networks, perceptrons, logistic regression, activation and loss functions, gradient descent, and provides end‑to‑end Python implementations using NumPy, TensorFlow, and Keras to build and evaluate a cat‑vs‑non‑cat classifier, complete with code snippets, visual explanations, and performance analysis.

Deep LearningKerasLogistic Regression
0 likes · 29 min read
Deep Learning from Theory to Practice: Neural Networks, Logistic Regression, TensorFlow and Keras for Cat Image Classification
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 19, 2019 · Artificial Intelligence

How RE2 Boosts FAQ Chatbot Accuracy: A Deep Dive into Text Matching Models

This article explains the design and evaluation of RE2, a lightweight yet expressive text‑matching framework for FAQ‑style chatbots, detailing its five‑layer architecture, block‑wise residual connections, experimental results on SNLI, MultiNLI, SciTail, Quora and WikiQA datasets, and its significant performance improvements in Alibaba’s DingXiaoMi service.

Deep LearningFAQ chatbotIndustrial AI
0 likes · 13 min read
How RE2 Boosts FAQ Chatbot Accuracy: A Deep Dive into Text Matching Models
Didi Tech
Didi Tech
Aug 17, 2019 · Artificial Intelligence

Didi’s Elastic Inference Service & IFX Engine: Achieving World‑Class AI Inference

Didi’s Elastic Inference Service (EIS) and its IFX AI acceleration engine provide a distributed, cost‑effective inference platform that automatically scales resources based on QPS and latency requirements, supports major deep‑learning frameworks, excels in public‑cloud, private‑cloud, IoT and edge scenarios, and achieved top‑rank DAWNBench latency and cost scores on ImageNet with P4 GPUs.

AI InferenceCloud AIDeep Learning
0 likes · 7 min read
Didi’s Elastic Inference Service & IFX Engine: Achieving World‑Class AI Inference
Big Data Technology Architecture
Big Data Technology Architecture
Aug 15, 2019 · Artificial Intelligence

Why Swift May Be the Next Big Thing in Deep Learning

The article explains why Google created Swift for TensorFlow, highlights Swift's strong backing, built‑in automatic differentiation, high performance comparable to C, seamless interoperability with Python, C and C++, low‑level hardware access, and its future role within the MLIR compiler ecosystem for deep learning.

Artificial IntelligenceDeep LearningMLIR
0 likes · 6 min read
Why Swift May Be the Next Big Thing in Deep Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 15, 2019 · Artificial Intelligence

How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training

This article introduces Auto Risk, a deep‑learning risk model for behavior‑sequence data that leverages unsupervised pre‑training with proxy tasks, details its convolution‑attention encoder, demonstrates significant gains across multiple business scenarios, and highlights its strong small‑sample and analogy capabilities.

Deep LearningRisk ModelingUnsupervised Learning
0 likes · 20 min read
How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 14, 2019 · Artificial Intelligence

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

This article presents a co-designed algorithm‑system solution—MIMN and an independent UIC module—that enables ultra‑long user behavior modeling for click‑through rate prediction, delivering significant offline AUC gains and online CTR/RPM improvements in Alibaba's display advertising platform.

CTR predictionDeep LearningRecommendation Systems
0 likes · 12 min read
How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction
Tencent Cloud Developer
Tencent Cloud Developer
Aug 6, 2019 · Cloud Computing

Tencent Cloud AIoT Product: Edge AI Capabilities and Cloud-Edge Collaboration Architecture

Tencent Cloud’s AIoT solution combines edge AI processing with a cloud‑edge collaboration framework, using container‑orchestrated microservices, AI chips and IoT connectivity to cut latency to milliseconds, lower bandwidth by sending only structured data, and enable real‑time applications such as smart retail, manufacturing, agriculture and building security.

AIoTContainer OrchestrationDeep Learning
0 likes · 28 min read
Tencent Cloud AIoT Product: Edge AI Capabilities and Cloud-Edge Collaboration Architecture
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 30, 2019 · Artificial Intelligence

Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences

This article introduces Auto Risk, a behavior‑sequence deep‑learning framework that uses unsupervised pre‑training with proxy tasks to learn universal feature representations from massive unlabeled data, achieving significant gains in risk‑control scenarios, improving AUC, supporting multi‑scene generalization and small‑sample learning.

Deep LearningRisk ModelingUnsupervised Learning
0 likes · 20 min read
Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences
Tencent Cloud Developer
Tencent Cloud Developer
Jul 25, 2019 · Artificial Intelligence

Three Waves of AI Development and Their Core Technologies

The article outlines AI’s three historical waves—search and reasoning, expert systems, and machine‑learning/deep‑learning—detailing their core technologies, achievements, and limitations, while emphasizing how past cycles inform today’s narrow AI advances and the renewed relevance of computing power and data‑driven methods.

AI historyArtificial IntelligenceDeep Learning
0 likes · 19 min read
Three Waves of AI Development and Their Core Technologies
Suning Technology
Suning Technology
Jul 24, 2019 · Artificial Intelligence

Multi‑Scale Body‑Part Masks Revolutionize Person Re‑Identification at CVPR 2019

At CVPR 2019 in Long Beach, Suning’s AI team presented a breakthrough paper on multi‑scale body‑part mask guided attention for person re‑identification, detailing the conference’s selectivity, the challenges of re‑identification, and how their deep‑learning approach achieves state‑of‑the‑art performance.

Attention MechanismCVPR 2019Deep Learning
0 likes · 5 min read
Multi‑Scale Body‑Part Masks Revolutionize Person Re‑Identification at CVPR 2019
Amap Tech
Amap Tech
Jul 23, 2019 · Artificial Intelligence

Traffic Sign Detection in Gaode Maps: Machine Learning Techniques and System Architecture

Gaode Maps uses a two-stage machine‑learning pipeline (Faster‑RCNN with shape‑based region proposal networks and fine‑grained classifiers) to detect hundreds of traffic‑sign types in billions of street‑view images, achieving high recall and precision, scalable updates, and near‑real‑time map data refresh.

AIDeep LearningFaster R-CNN
0 likes · 11 min read
Traffic Sign Detection in Gaode Maps: Machine Learning Techniques and System Architecture
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 9, 2019 · Artificial Intelligence

Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works

This article systematically reviews the evolution of attention mechanisms—from early additive and multiplicative forms to self‑attention and multi‑head variants—explaining their core three‑step framework, key differences, and why they have become essential across NLP, vision, and broader AI applications.

Deep LearningMachine LearningNLP
0 likes · 19 min read
Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 5, 2019 · Artificial Intelligence

iQIYI Multimodal Person Recognition Competition: 91.14% Accuracy Achieved by BUPT Team

After a three‑month contest co‑hosted by iQIYI and ACM MM, 255 teams competed on the challenging iQIYI‑VID‑2019 multimodal dataset, and the BUPT Automation School team won with a 91.14% person‑recognition accuracy, advancing the field and enhancing iQIYI’s video recommendation and AI services.

AI CompetitionDeep Learningaccuracy
0 likes · 6 min read
iQIYI Multimodal Person Recognition Competition: 91.14% Accuracy Achieved by BUPT Team
360 Tech Engineering
360 Tech Engineering
Jul 2, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide

This article explains how TensorFlow powers Huajiao Live's recommendation system, introduces the TensorSlow project for demystifying TensorFlow's core, and walks through deep‑learning fundamentals, computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and building a multi‑layer perceptron with Python code examples.

Computational GraphDeep LearningMLP
0 likes · 13 min read
Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide
Huajiao Technology
Huajiao Technology
Jul 2, 2019 · Artificial Intelligence

Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation

This article introduces deep learning applications at Huajiao Live, explains TensorFlow's computational graph architecture, details core concepts such as placeholders, variables, operations, forward and backward propagation, and provides complete Python-like code examples for building and training a multi-layer perceptron.

Computational GraphDeep LearningMLP
0 likes · 14 min read
Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation
Xianyu Technology
Xianyu Technology
Jun 27, 2019 · Frontend Development

Image-to-UI Code Generation Demo and Architecture Overview

The Xianyu team’s new “black‑tech” system automatically transforms UI mockup images into production‑ready code by detecting components with deep‑learning models, extracting layouts via OpenCV, and employing a modular stream‑oriented architecture of units, tasks, and server layers that enables rapid testing, flexible composition, and future enhancements such as improved container recognition and semantic understanding.

Deep LearningUI generationfrontend
0 likes · 7 min read
Image-to-UI Code Generation Demo and Architecture Overview
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 20, 2019 · Artificial Intelligence

Unlock Cutting-Edge Voice AI: Highlights from Alibaba’s Speech & Signal Processing eBook

This article introduces Alibaba's new e‑book collection of five ICASSP‑accepted papers that showcase advances in speech recognition, synthesis, and emotion detection, detailing novel models like DFSMN, A‑LSTM, and speaker‑adaptation techniques that dramatically improve speed, size, and accuracy.

AI voiceDeep LearningEmotion Recognition
0 likes · 6 min read
Unlock Cutting-Edge Voice AI: Highlights from Alibaba’s Speech & Signal Processing eBook
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 12, 2019 · Artificial Intelligence

How Alibaba’s PAISoar Accelerates Deep Learning: 101× Speedup on 128 GPUs

Alibaba engineers detail the PAISoar distributed training framework, showing how RDMA‑optimized hardware, Ring AllReduce algorithms, and user‑friendly APIs boost deep‑learning models—like the GreenNet CNN—to 101‑fold speedups on 128 GPUs, dramatically reducing training time from days to under a day.

AI infrastructureDeep LearningDistributed Training
0 likes · 17 min read
How Alibaba’s PAISoar Accelerates Deep Learning: 101× Speedup on 128 GPUs
Tencent Cloud Developer
Tencent Cloud Developer
Jun 5, 2019 · Artificial Intelligence

Tencent Cloud OCR Technology: Principles, Challenges, and Industry Applications

Tencent Cloud OCR leverages deep‑learning‑based text detection and recognition, including Compact Inception and multi‑layer RNN refinements, to overcome challenges such as complex backgrounds, low resolution, and multilingual layouts, delivering over 90% accuracy for ID cards, bank cards, business licenses, handwritten text, and powering fast, cost‑saving applications in logistics, QQ, and WeChat Work.

Deep LearningOCROptical Character Recognition
0 likes · 7 min read
Tencent Cloud OCR Technology: Principles, Challenges, and Industry Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 5, 2019 · Artificial Intelligence

Tracing the Evolution of Language Models: From N‑grams to GPT‑2

This article reviews the historical development of natural language processing language models, covering expert rule‑based systems, statistical n‑grams, smoothing techniques, neural network models such as NNLM, RNN, word2vec, GloVe, ELMo, and the transformer‑based breakthroughs of GPT, BERT and GPT‑2, and summarizes their impact on modern NLP tasks.

BERTDeep LearningGPT
0 likes · 25 min read
Tracing the Evolution of Language Models: From N‑grams to GPT‑2
Youku Technology
Youku Technology
May 29, 2019 · Artificial Intelligence

Youku Video Enhancement and Super-Resolution Competition Announcement

The Youku Video Enhancement and Super‑Resolution Challenge invites teams to develop models that restore low‑resolution, noisy video to high‑definition quality using a 10,000‑pair industry dataset, offering up to RMB 100,000 in prizes and a recruitment pathway, with registration open through June 16 and competition phases spanning May to September.

AI CompetitionDeep LearningYouku
0 likes · 10 min read
Youku Video Enhancement and Super-Resolution Competition Announcement
Alibaba Cloud Developer
Alibaba Cloud Developer
May 27, 2019 · Artificial Intelligence

From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP

This article walks through the development of deep learning for natural language processing, starting with basic neural cells and shallow networks, then exploring CNNs, RNNs, LSTMs, TextCNN, ESIM, ELMo, and culminating with the Transformer‑based BERT model, its training objectives, fine‑tuning strategies, and performance comparisons.

BERTCNNDeep Learning
0 likes · 19 min read
From Neurons to BERT: Tracing the Evolution of Deep Learning in NLP