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HyperAI Super Neural
HyperAI Super Neural
May 18, 2026 · Artificial Intelligence

LSTM Surrogate Model Accelerates Second‑Order Nonlinear Optics Simulations by 252× to Millisecond Scale

A team from Stanford, UCLA and SLAC built a high‑fidelity LSTM surrogate that predicts sum‑frequency‑generation fields with millisecond‑level latency, achieving a 252‑fold speedup over split‑step Fourier simulations while preserving sub‑percent accuracy across thousands of pulse‑shaping configurations.

LSTMMachine Learningnonlinear optics
0 likes · 11 min read
LSTM Surrogate Model Accelerates Second‑Order Nonlinear Optics Simulations by 252× to Millisecond Scale
Data Party THU
Data Party THU
May 12, 2026 · Artificial Intelligence

Time Series Large Models Explained: What They Are and Why They Matter

The article introduces time‑series data, its ubiquitous examples, the challenges of traditional small models, and proposes a universal time‑series large model that simplifies data preparation and model building, ultimately enabling more efficient and stable industrial AI solutions, now offered as a cloud service.

AIARIMACRISP-DM
0 likes · 6 min read
Time Series Large Models Explained: What They Are and Why They Matter
AI Cyberspace
AI Cyberspace
Feb 11, 2026 · Artificial Intelligence

From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch

This tutorial explains the nature of sequential data, why traditional feed‑forward networks struggle with it, and how recurrent architectures such as RNN, LSTM, and GRU capture temporal dependencies, complete with mathematical foundations, training algorithms, and full PyTorch implementations for sentiment analysis, text generation, and encoder‑decoder models.

Encoder-DecoderGRULSTM
0 likes · 57 min read
From RNNs to LSTMs and GRUs: A Hands‑On Guide to Sequence Modeling in PyTorch
AI Cyberspace
AI Cyberspace
Jan 13, 2026 · Artificial Intelligence

From Symbolic AI to LLMs: A Complete NLP History and Model Guide

This article provides a comprehensive overview of natural language processing, tracing its evolution from early symbolic and statistical stages through deep learning breakthroughs, detailing sequence models, key NLP tasks, text representation methods, and the development of modern architectures like RNN, LSTM, GRU, Transformer, and GPT series.

GPTLSTMNLP
0 likes · 60 min read
From Symbolic AI to LLMs: A Complete NLP History and Model Guide
Xiaomi Tech
Xiaomi Tech
Dec 29, 2025 · Industry Insights

Xiaomi’s Standard‑Size Partition‑Wash Technology Achieves International Lead

Xiaomi’s washing‑machine project, evaluated by the China Light Industry Federation, showcases three core innovations—independent drum suspension, a high‑integration water system, and an AI‑driven full‑frequency drive—that together deliver low‑noise, high‑cleanliness performance and are deemed internationally leading.

AI controlLSTMXiaomi
0 likes · 5 min read
Xiaomi’s Standard‑Size Partition‑Wash Technology Achieves International Lead
Qborfy AI
Qborfy AI
Aug 7, 2025 · Artificial Intelligence

Understanding RNNs: From Memory Cells to Real‑World Applications

This article explains how recurrent neural networks (RNNs) add memory to neural models, details the gate mechanisms of LSTM and GRU, compares their structures and parameter counts, and illustrates their use in speech recognition, translation, stock prediction, and video generation, while highlighting practical insights and energy considerations.

AIGRULSTM
0 likes · 5 min read
Understanding RNNs: From Memory Cells to Real‑World Applications
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 6, 2025 · Artificial Intelligence

How Transformers Revolutionize Sequence Modeling: From RNN Limits to Self‑Attention Mastery

This article explains why Transformer models surpass traditional RNN‑based seq2seq architectures by introducing self‑attention, multi‑head attention, and positional encoding, detailing the inner workings of encoders, decoders, and attention mechanisms, and comparing their advantages and limitations across NLP and vision tasks.

GRULSTMRNN
0 likes · 30 min read
How Transformers Revolutionize Sequence Modeling: From RNN Limits to Self‑Attention Mastery
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 12, 2023 · Artificial Intelligence

A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models

This article provides a thorough overview of large language models, explaining the relationship between NLP and LLMs, the evolution from RNN to LSTM, the fundamentals of attention mechanisms, and the architecture and operation of Transformer models, all illustrated with clear examples and diagrams.

Artificial IntelligenceLSTMNLP
0 likes · 25 min read
A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models
DataFunTalk
DataFunTalk
Oct 16, 2023 · Artificial Intelligence

Personalized Title Generation and Automatic Cover Image Synthesis for Information‑Flow Scenarios

This article presents technical approaches for generating personalized article titles and automatically synthesizing cover images, covering keyword‑based, click‑sequence‑based, and author‑style‑based title models, as well as image restoration, key‑information extraction, object detection, and layout generation techniques to improve user engagement in recommendation and search systems.

AI recommendationLSTMTransformer
0 likes · 11 min read
Personalized Title Generation and Automatic Cover Image Synthesis for Information‑Flow Scenarios
Ctrip Technology
Ctrip Technology
Aug 3, 2023 · Operations

Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering

The article describes Ctrip's AIOps approach to improving alert quality by combining statistical methods and machine‑learning models such as LSTM, trend analysis, adaptive threshold calculation, and dynamic‑time‑warping based periodic anomaly detection, achieving significant gains in precision and fault‑recall rates.

Anomaly DetectionLSTMMonitoring
0 likes · 12 min read
Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering
Ctrip Technology
Ctrip Technology
May 18, 2023 · Artificial Intelligence

LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip

This article presents Ctrip's end‑to‑end solution for precise ad‑inventory forecasting using an LSTM model combined with entity embedding, covering data preprocessing, K‑means clustering, model architecture, offline‑online incremental training, early‑stop mechanisms, evaluation metrics, and Python service deployment.

EmbeddingLSTMPyTorch
0 likes · 19 min read
LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip
DataFunTalk
DataFunTalk
Apr 3, 2023 · Artificial Intelligence

Implementing RNN, LSTM, and GRU with PyTorch

This article introduces the basic architectures of recurrent neural networks (RNN), LSTM, and GRU, explains PyTorch APIs such as nn.RNN, nn.LSTM, nn.GRU, details their parameters, demonstrates code examples for building and testing these models, and provides practical insights for deep learning practitioners.

GRULSTMPyTorch
0 likes · 9 min read
Implementing RNN, LSTM, and GRU with PyTorch
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting

The paper proposes clustering thousands of related time series and training separate global LSTM models for each cluster, showing that this reduces heterogeneity, leverages shared information, and improves forecasting accuracy compared to individual models, with extensive experiments on CIF2016 and NN5 datasets.

LSTMMachine LearningRNN
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
JD Cloud Developers
JD Cloud Developers
Nov 7, 2022 · Artificial Intelligence

Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion

This article presents a threshold‑free anomaly detection framework for streaming time series that combines an LSTM‑based baseline module with an unsupervised detection module, detailing the architecture, training process, data preprocessing, and experimental results that demonstrate superior accuracy and F1 scores.

Anomaly DetectionLSTMMachine Learning
0 likes · 15 min read
Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion
Ctrip Technology
Ctrip Technology
Oct 20, 2022 · Artificial Intelligence

Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions

This article presents a pandemic‑aware method for predicting national hotel monthly room‑nights over the next six months, detailing data augmentation, feature engineering, LSTM and SARIMA‑LASSO modeling, scenario‑based risk assessment, and evaluation results that demonstrate accurate forecasts despite COVID‑19 disruptions.

AILSTMPandemic Impact
0 likes · 14 min read
Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions
Model Perspective
Model Perspective
Oct 6, 2022 · Artificial Intelligence

Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting

This article explains the structure and operation of recurrent neural networks (RNNs), their limitations, how long short‑term memory (LSTM) networks overcome these issues with gated mechanisms, and provides a complete Python implementation for time‑series airline passenger forecasting.

LSTMPythonRNN
0 likes · 17 min read
Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting
Model Perspective
Model Perspective
Aug 15, 2022 · Artificial Intelligence

Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras

This article introduces recurrent neural networks (RNNs) and their ability to handle sequential data, explains the limitations of vanilla RNNs, presents the LSTM architecture with its gates, and provides complete Keras code for data loading, model building, and training both vanilla RNN and LSTM models.

KerasLSTMRNN
0 likes · 5 min read
Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras
DataFunSummit
DataFunSummit
Apr 23, 2022 · Artificial Intelligence

Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform

The article describes how a logistics company built a data‑driven platform that uses big‑data storage, DeepFM and LSTM models, and real‑time GPS tracking to create an intelligent vehicle‑cargo matching system, a multi‑label driver tagging framework, and a freight price prediction engine, thereby improving efficiency and reducing costs across the industry.

LSTMLogisticsMachine Learning
0 likes · 16 min read
Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform
DataFunSummit
DataFunSummit
Oct 15, 2021 · Artificial Intelligence

Risk Control and Operations for Existing Loan Customers

This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing customer flow during the pandemic, policy impacts, rapid deterioration patterns, and by applying advanced models such as LSTM, survival analysis, and B‑card strategies to enable timely risk detection and targeted cross‑selling.

LSTMbehavior modelingcustomer operations
0 likes · 19 min read
Risk Control and Operations for Existing Loan Customers
58 Tech
58 Tech
Oct 12, 2021 · Artificial Intelligence

Seq2Seq Approaches for Phone Number Extraction from Two‑Speaker Voice Dialogues

This article presents a practical study of extracting phone numbers from two‑speaker voice dialogues using Seq2Seq models—including LSTM, GRU with attention and feature fusion, and Transformer—detailing data characteristics, model architectures, training strategies, experimental results, and comparative analysis showing the GRU‑Attention approach achieving the best performance.

GRULSTMNLP
0 likes · 13 min read
Seq2Seq Approaches for Phone Number Extraction from Two‑Speaker Voice Dialogues
58 Tech
58 Tech
Jan 27, 2021 · Artificial Intelligence

Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot

This article details the BangBang intelligent customer service system's overall architecture, core capabilities, knowledge‑base construction, and successive model upgrades—from FastText to TextCNN, Bi‑LSTM, and model fusion—showing how each iteration improved accuracy, recall, and F1 scores toward a stable 95% performance level.

AILSTMTextCNN
0 likes · 12 min read
Model Iteration and Architecture of the BangBang Intelligent Customer Service QABot
DataFunTalk
DataFunTalk
Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeEmbeddingLSTM
0 likes · 19 min read
User Preference Mining and Modeling Practices at Beike
Tencent Advertising Technology
Tencent Advertising Technology
Jul 30, 2020 · Artificial Intelligence

Tencent Advertising Algorithm Competition 2020: Problem Overview, Data, Model Implementation, and Results

This article details the 2020 Tencent Advertising Algorithm Competition, describing the user profiling task, data fields, feature engineering, Python code for ID mapping and Word2Vec training, multiple model architectures (LSTM, CNN-Inception, transformer), and the final performance results achieved by the team.

AdvertisingCNNLSTM
0 likes · 13 min read
Tencent Advertising Algorithm Competition 2020: Problem Overview, Data, Model Implementation, and Results
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 24, 2020 · Artificial Intelligence

Fine‑grained Character Sentiment Analysis in Scripts: Models, Challenges, and Future Directions

The article surveys fine‑grained character sentiment analysis for script evaluation, detailing traditional, target‑dependent and aspect‑level methods, describing iQIYI’s BERT‑TD‑LSTM and CNN architectures, addressing challenges such as character name recognition and long‑range context, and outlining future improvements after a Parasite case study.

BERTLSTMNLP
0 likes · 19 min read
Fine‑grained Character Sentiment Analysis in Scripts: Models, Challenges, and Future Directions
Fulu Network R&D Team
Fulu Network R&D Team
Jun 11, 2020 · Artificial Intelligence

Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting

This article presents an intelligent inventory management solution that predicts product consumption using two time‑series algorithms—Facebook's Prophet and LSTM deep learning—detailing data sources, preprocessing, model configuration, evaluation metrics, and a comparative analysis of their performance and suitability.

Data AnalysisLSTMMachine Learning
0 likes · 16 min read
Intelligent Inventory Management: Comparing Prophet and LSTM for Time‑Series Forecasting
Amap Tech
Amap Tech
Jun 9, 2020 · Artificial Intelligence

Deep Learning Approach for Route ETA Prediction in Navigation

The article proposes a deep‑learning framework that uses an LSTM to predict segment‑level travel times and fully‑connected layers to aggregate them into a full‑route ETA, demonstrating on Beijing data a 28.2% MSE reduction and superior accuracy over traditional regressors by capturing temporal and network dependencies.

ETALSTMPrediction
0 likes · 7 min read
Deep Learning Approach for Route ETA Prediction in Navigation
58 Tech
58 Tech
Jun 3, 2020 · Artificial Intelligence

Speaker Verification System for Detecting Spam Calls in 58 Used‑Car Platform

This article describes how the 58 used‑car team built a speaker‑verification pipeline—covering data collection, MFCC feature extraction, LSTM and GMM modeling, threshold tuning, multi‑speaker clustering, and deployment results—to automatically block nuisance telemarketing calls while preserving user privacy.

GMMLSTMMFCC
0 likes · 15 min read
Speaker Verification System for Detecting Spam Calls in 58 Used‑Car Platform
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 27, 2020 · Artificial Intelligence

Advertising Inventory Estimation and Allocation Techniques at iQIYI: From ARIMA to Deep Learning and AI Tagging

iQIYI’s brand‑advertising system combines statistical and ARIMA‑based forecasting, adaptive and deep‑learning models, factorization‑machine regression, large‑scale bipartite‑graph allocation, hierarchical handling of long‑tail dimensions, frequency‑capping constraints, and an AI‑driven video‑tagging pipeline to accurately estimate inventory and dynamically place ads.

AI taggingARIMAAdvertising
0 likes · 26 min read
Advertising Inventory Estimation and Allocation Techniques at iQIYI: From ARIMA to Deep Learning and AI Tagging
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 26, 2020 · Artificial Intelligence

How Amap Uses AI to Automate Millions of User Feedback Reports

This article describes how Gaode Map leverages machine‑learning techniques—such as word2vec embeddings, LSTM networks, fine‑tuning, and confidence‑threshold ensembles—to automatically classify and verify massive user‑feedback intelligence, streamlining the multi‑step workflow from data collection to road‑map updates and dramatically improving efficiency.

AILSTMNLP
0 likes · 16 min read
How Amap Uses AI to Automate Millions of User Feedback Reports
Efficient Ops
Efficient Ops
Mar 25, 2020 · Operations

How JD Logistics Built a 300‑Million‑Metric Real‑Time Monitoring System for 99.999% Uptime

This article details JD Logistics' journey to design and implement a massive, AI‑enhanced monitoring platform that handles over three million metrics across hundreds of warehouses, addressing challenges of scale, network complexity, frequent asset changes, and integrating AIOps for proactive fault detection and resolution.

Anomaly DetectionCMDBKafka
0 likes · 23 min read
How JD Logistics Built a 300‑Million‑Metric Real‑Time Monitoring System for 99.999% Uptime
dbaplus Community
dbaplus Community
Mar 9, 2020 · Artificial Intelligence

How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency

This article details a deep‑learning‑driven, real‑time alert system that combines TensorFlow LSTM time‑series forecasting with Spark Streaming to achieve high‑coverage, low‑latency anomaly detection for large‑scale data‑ops environments, including data preprocessing, metric classification, model training, and deployment pipelines.

AI OpsAnomaly DetectionLSTM
0 likes · 18 min read
How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency
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.

BMUFCTCLSTM
0 likes · 15 min read
TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models
Architecture Digest
Architecture Digest
Jan 28, 2020 · Artificial Intelligence

Predicting COVID-19 Cases Using LSTM Based on SARS Data: Methodology and Evaluation

This article investigates whether a short‑term time‑series algorithm, specifically an LSTM model trained on limited SARS data, can predict and assess COVID‑19 case numbers, describing data collection, model training, experimental validation, error analysis, and practical implications of the findings.

COVID-19 predictionData AnalysisLSTM
0 likes · 11 min read
Predicting COVID-19 Cases Using LSTM Based on SARS Data: Methodology and Evaluation
Amap Tech
Amap Tech
Jan 3, 2020 · Artificial Intelligence

Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)

Amap replaced its rule‑based feedback pipeline with a three‑stage, LSTM‑driven system that combines word2vec embeddings and structured fields, achieving over 96% classification accuracy, cutting manual workload by 80%, and slashing per‑task costs while enabling scalable, data‑driven map quality improvements.

Gaode MapsLSTMMachine Learning
0 likes · 14 min read
Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 21, 2019 · Artificial Intelligence

Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring

The article reviews traditional, neural network, and open‑source time‑series forecasting methods, explains their strengths and limitations, and demonstrates how NetEase applies short‑term and long‑term prediction models such as Holt‑Winters, ARIMA, STL, Prophet, and LSTM to improve game monitoring and proactive alerting.

ARIMAHolt-WintersLSTM
0 likes · 12 min read
Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring
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 ForecastingLSTMTime-series
0 likes · 10 min read
Advertising Inventory Forecasting Using an LSTM-Based Deep Learning Model
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.

CNNGRULSTM
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
Amap Tech
Amap Tech
Dec 6, 2019 · Artificial Intelligence

Semantic Understanding of Merchant Signboards for Automatic POI Name Generation at Amap

Amap's POI naming automation uses a two-stage cascade model: Stage 1 extracts token and sentence features with POS tags and domain-adapted BERT‑POI; Stage 2 employs a Bi‑LSTM to model line relationships, achieving over 95% semantic accuracy and 3‑6% recall improvements, thereby enhancing automatic signboard‑based POI name generation.

BERTLSTMName Generation
0 likes · 7 min read
Semantic Understanding of Merchant Signboards for Automatic POI Name Generation at Amap
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.

LSTMPredictionStarNet
0 likes · 15 min read
StarNet: Global Interaction Network for Pedestrian Trajectory Prediction
Meituan Technology Team
Meituan Technology Team
Oct 31, 2019 · Artificial Intelligence

Meituan Unmanned Delivery Team Wins CVPR 2019 Obstacle Trajectory Prediction Challenge – Methodology Overview

Meituan’s unmanned‑delivery team won the CVPR 2019 Trajectory Prediction Challenge by using a multi‑class independent LSTM encoder‑decoder with Gaussian‑noise augmentation, discarding size and noisy orientation data, applying rotation and interpolation augmentations, and achieving a weighted ADE of 1.3425, surpassing StarNet and TrafficPredict, with plans to explore interaction‑based and graph‑neural‑network models.

CVPR 2019LSTMMeituan
0 likes · 9 min read
Meituan Unmanned Delivery Team Wins CVPR 2019 Obstacle Trajectory Prediction Challenge – Methodology Overview
HomeTech
HomeTech
Aug 7, 2019 · Artificial Intelligence

Near-Duplicate Video Retrieval: Framework, Feature Extraction, Metric Learning, and Model Optimization

This article presents a comprehensive study of near‑duplicate video retrieval, covering the definition of near‑duplicate videos, motivations for deduplication, challenges, a two‑stage offline/online processing framework, keyframe and VGG16‑based feature extraction, metric‑learning loss functions, training procedures, dataset preparation, evaluation metrics, and model enhancements using LSTM and attention mechanisms.

LSTMMAPVGG16
0 likes · 12 min read
Near-Duplicate Video Retrieval: Framework, Feature Extraction, Metric Learning, and Model Optimization
Tencent Cloud Developer
Tencent Cloud Developer
Apr 24, 2019 · Artificial Intelligence

Chinese Text Sentiment Classification Using Multi‑layer LSTM: Data Preparation, Model Architecture, and Business Applications

The article details a practical workflow for Chinese sentiment classification in Tencent’s Goose Man product, covering data preparation, word‑segmentation challenges, a six‑layer multi‑LSTM architecture with word embeddings, training results achieving roughly 96 % accuracy, and its deployment for automatic detection of misleading and high‑impact user reviews.

Chinese NLPKerasLSTM
0 likes · 23 min read
Chinese Text Sentiment Classification Using Multi‑layer LSTM: Data Preparation, Model Architecture, and Business Applications
Hulu Beijing
Hulu Beijing
Mar 12, 2019 · Artificial Intelligence

How LSTM Networks Achieve Long‑Term Memory: Mechanisms Explained

This article explains how Long Short‑Term Memory (LSTM) networks overcome the long‑term dependency problem of traditional recurrent neural networks by using gated cells that store and retrieve information over extended periods, and highlights their widespread applications in speech recognition, machine translation, image captioning, and beyond.

Artificial IntelligenceLSTMLong Short-Term Memory
0 likes · 5 min read
How LSTM Networks Achieve Long‑Term Memory: Mechanisms Explained
58 Tech
58 Tech
Feb 20, 2019 · Artificial Intelligence

Building and Deploying Language Models for Text Quality Evaluation and Generation

This article explains the concepts, training pipeline, deployment formats, and practical applications of language models—particularly LSTM‑based models—for evaluating and generating text quality in a real‑world rental listing platform, highlighting data preparation, model training, and online serving techniques.

DeploymentLSTMPerplexity
0 likes · 16 min read
Building and Deploying Language Models for Text Quality Evaluation and Generation
JD Tech Talk
JD Tech Talk
Jan 16, 2019 · Artificial Intelligence

Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results

This article presents a detailed case study of building a purchase‑user prediction model by integrating Convolutional Neural Networks for feature extraction with Long Short‑Term Memory networks for time‑series forecasting, covering background, model structure, data augmentation, experimental results, and business impact.

CNNLSTMTime-series
0 likes · 10 min read
Combining CNN and LSTM for Purchase User Prediction: Architecture, Implementation, and Results
360 Quality & Efficiency
360 Quality & Efficiency
Dec 21, 2018 · Artificial Intelligence

Machine Learning-Based Test Case Step Recommendation: Data Preprocessing, N‑gram, CBOW, and RNN/LSTM Model Construction

This article explains how to use machine‑learning techniques—including data preprocessing, N‑gram, CBOW, and various RNN/LSTM models—to automatically recommend the next function in a test‑case step sequence, improving writing speed and efficiency for developers.

CBOWLSTMMachine Learning
0 likes · 4 min read
Machine Learning-Based Test Case Step Recommendation: Data Preprocessing, N‑gram, CBOW, and RNN/LSTM Model Construction
Ctrip Technology
Ctrip Technology
Dec 19, 2018 · Artificial Intelligence

Anomaly Detection in Operations: From Statistical Rules to Deep Learning with LSTM and Real‑Time Streaming

This article describes how a large‑scale operations monitoring system at Ctrip evolved from rule‑based alerts to a deep‑learning‑driven anomaly detection pipeline using LSTM models and Flink streaming, achieving a ten‑fold reduction in alert volume while improving fault detection.

Anomaly DetectionFlinkLSTM
0 likes · 14 min read
Anomaly Detection in Operations: From Statistical Rules to Deep Learning with LSTM and Real‑Time Streaming
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 30, 2018 · Artificial Intelligence

How Advanced LSTM (A‑LSTM) Boosts Speech Emotion Recognition by 5.5%

This article introduces Advanced LSTM (A‑LSTM), which linearly combines multiple past hidden states to overcome traditional LSTM's one‑step dependency, and demonstrates its application in utterance‑level speech emotion recognition, achieving a 5.5% accuracy improvement through attention‑based weighted‑pooling RNNs and auxiliary speaker and gender tasks.

A-LSTMLSTMRNN
0 likes · 8 min read
How Advanced LSTM (A‑LSTM) Boosts Speech Emotion Recognition by 5.5%
dbaplus Community
dbaplus Community
Oct 21, 2018 · Artificial Intelligence

How Weibo’s Hubble Platform Uses AI for Real‑Time Monitoring and Trend Forecasting

The article details Weibo Advertising's Hubble monitoring system, describing its three‑layer architecture, metric taxonomy, AI‑driven trend prediction with LSTM models, dynamic alert thresholds, and performance testing using GoReplay, illustrating how large‑scale data and machine learning enable proactive operations.

AILSTMMonitoring
0 likes · 22 min read
How Weibo’s Hubble Platform Uses AI for Real‑Time Monitoring and Trend Forecasting
AntTech
AntTech
Sep 7, 2018 · Artificial Intelligence

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

This article explains how Alipay combats the surge of mobile payment fraud by upgrading its risk‑identification system with deep‑learning techniques, modeling victim and fraudster behavior sequences using LSTM, and integrating the resulting scores into existing models to achieve a measurable increase in detection coverage.

LSTMbehavior sequencedeep learning
0 likes · 11 min read
How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection
JD Tech
JD Tech
Aug 14, 2018 · Artificial Intelligence

GCN‑LSTM Image Captioning Model by JD AI Research Institute

JD AI Research Institute presented a GCN‑LSTM encoder‑decoder system that integrates object semantic and spatial relationships via graph convolutional networks to significantly improve image captioning performance on the COCO benchmark, achieving state‑of‑the‑art results.

COCO datasetImage CaptioningLSTM
0 likes · 7 min read
GCN‑LSTM Image Captioning Model by JD AI Research Institute
Hulu Beijing
Hulu Beijing
Dec 12, 2017 · Artificial Intelligence

How LSTM Achieves Long‑Term Memory: Gates, Activations & Variants Explained

This article explains how LSTM networks overcome RNN limitations by using input, forget, and output gates with sigmoid and tanh activations, describes the core update equations, discusses alternative activation functions and hard‑gate variants, and provides references for deeper study.

LSTMRNNSequence Modeling
0 likes · 10 min read
How LSTM Achieves Long‑Term Memory: Gates, Activations & Variants Explained
ITPUB
ITPUB
Nov 17, 2017 · Artificial Intelligence

How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study

This article explains how Baidu Waimai's risk‑control team uses recurrent neural networks, especially LSTM, within TensorFlow to detect fraudulent merchants and users, compares static and dynamic RNN implementations, demonstrates a MNIST digit‑recognition example, and discusses optimization algorithms and model trade‑offs for real‑time fraud detection.

LSTMMNISTOptimization
0 likes · 27 min read
How RNNs Power Risk Control in O2O Food Delivery: A TensorFlow Case Study
Qunar Tech Salon
Qunar Tech Salon
Apr 27, 2017 · Artificial Intelligence

LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling

The paper introduces LSTM‑Jump, a reinforcement‑learning‑trained LSTM variant that can dynamically skip irrelevant tokens, achieving up to six‑fold speed‑ups over standard sequential LSTMs while maintaining or improving accuracy on various NLP tasks such as sentiment analysis, document classification, and question answering.

LSTMNLPSequence Modeling
0 likes · 7 min read
LSTM‑Jump: Learning to Skim Text for Faster Sequence Modeling