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Model Optimization

117 articles · Page 2 of 2
Ctrip Technology
Ctrip Technology
Nov 12, 2020 · Artificial Intelligence

Ctrip Machine Translation Platform: Architecture, Data Construction, Algorithm Design, and Performance Optimization

This article presents a comprehensive overview of Ctrip's multilingual machine translation platform, detailing demand analysis, system architecture, data pipeline, algorithmic innovations such as task‑space fusion and term‑translation interventions, as well as extensive performance optimizations for low‑resource languages.

AICtripMachine Translation
0 likes · 20 min read
Ctrip Machine Translation Platform: Architecture, Data Construction, Algorithm Design, and Performance Optimization
DataFunTalk
DataFunTalk
Aug 18, 2020 · Artificial Intelligence

COLD: A Next‑Generation Pre‑Ranking System for Online Advertising

The article introduces COLD, a computing‑power‑aware online and lightweight deep pre‑ranking system for Alibaba's targeted ads, detailing its evolution from static CTR models to vector‑inner‑product models, its flexible network architecture with feature‑selection via SE blocks, engineering optimizations such as parallelism, column‑wise computation, Float16 and MPS, and demonstrates superior offline and online performance through extensive experiments.

COLDModel OptimizationOnline Advertising
0 likes · 11 min read
COLD: A Next‑Generation Pre‑Ranking System for Online Advertising
360 Quality & Efficiency
360 Quality & Efficiency
Aug 7, 2020 · Artificial Intelligence

Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks

This article analyses the drawbacks of using fully‑connected layers in convolutional neural networks for image tasks, proposes fully‑convolutional alternatives with 1×1 convolutions and strategic max‑pooling, provides TensorFlow code examples, compares model sizes and performance, and discusses deployment considerations for variable‑size inputs.

CNNFully Convolutional NetworkModel Optimization
0 likes · 7 min read
Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks
Qunar Tech Salon
Qunar Tech Salon
May 13, 2020 · Artificial Intelligence

Intelligent Hotel Post‑Sale QA System: Model Selection, Evaluation, and Engineering Optimization

This article describes the design, model selection, experimental evaluation, and engineering optimization of an AI‑driven post‑sale question‑answering system for hotel services, covering FAQ construction, intent detection, deep‑learning matching models such as DSSM, ESIM, BERT, and their performance and latency trade‑offs.

AIBERTDSSM
0 likes · 14 min read
Intelligent Hotel Post‑Sale QA System: Model Selection, Evaluation, and Engineering Optimization
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 2, 2020 · Artificial Intelligence

How AutoML Transformed AR Scanning: Faster, Smaller, More Accurate Models

In 2020, the AR “scan‑for‑fortune” feature achieved a full AutoML rollout on the xNN‑Cloud platform, automating network architecture design and the entire model development pipeline, which cut Android inference time by over 50%, iOS by 30%, reduced model size, and boosted accuracy by 1.6% while handling billions of in‑client inferences.

AR VisionAutoMLMobileNet
0 likes · 15 min read
How AutoML Transformed AR Scanning: Faster, Smaller, More Accurate Models
Tencent Cloud Developer
Tencent Cloud Developer
Mar 6, 2020 · Artificial Intelligence

WeChat "Scan" Object Detection: Mobile AI Model Design, Optimization, and Deployment

The paper presents a lightweight, anchor‑free CenterNet‑based object‑ness detector for WeChat’s Scan feature, built on a ShuffleNetV2 backbone with enlarged 5×5 depth‑wise convolutions, a streamlined detection head, and a Pyramid Interpolation Module, then quantized, ONNX‑converted and NCNN‑deployed to achieve a 436 KB model running in ~15 ms per frame on an iPhone 8 CPU.

CenterNetModel OptimizationReal-time inference
0 likes · 12 min read
WeChat "Scan" Object Detection: Mobile AI Model Design, Optimization, and Deployment
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Dec 31, 2019 · Big Data

Apache Kylin Overview and Model Optimization Practices for Trajectory Analytics

This article introduces Apache Kylin, details its deployment at Tongcheng Yilong, explains the design of a large‑scale trajectory model, and provides step‑by‑step optimization techniques—including cube dimension reduction, HBase rowkey tuning, build parameter tweaks, high‑cardinality handling, and query compression disabling—to achieve sub‑second OLAP queries on multi‑terabyte data.

Apache KylinBig DataCube
0 likes · 17 min read
Apache Kylin Overview and Model Optimization Practices for Trajectory Analytics
Xianyu Technology
Xianyu Technology
Dec 11, 2019 · Artificial Intelligence

Improving Small Object Detection for UI2CODE via Data Augmentation and Model Optimization

The study enhances UI2CODE’s ability to detect tiny UI components by augmenting training data with copied small objects, upgrading the detector from Faster RCNN to FPN and Cascade FPN, and refining box positions with smoothing and projection, achieving superior small‑object mAP/mAR and enabling broader UI parsing applications.

Data AugmentationFPNModel Optimization
0 likes · 9 min read
Improving Small Object Detection for UI2CODE via Data Augmentation and Model Optimization
58 Tech
58 Tech
Nov 6, 2019 · Artificial Intelligence

TensorRT Acceleration and Integration Design for the 58 AI Platform (WPAI)

This article explains how the 58 AI platform leverages NVIDIA TensorRT to accelerate deep‑learning inference on GPUs, describes three integration approaches, details the TF‑TRT implementation and Kubernetes deployment, and presents performance gains for ResNet‑50 and OCR models.

AI platformGPU inferenceKubernetes deployment
0 likes · 7 min read
TensorRT Acceleration and Integration Design for the 58 AI Platform (WPAI)
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.

Model OptimizationTensorFlowTensorRT
0 likes · 13 min read
Accelerating TensorFlow Model Inference with NVIDIA TensorRT: Methods, Experiments, and Results
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 2, 2019 · Artificial Intelligence

How MNN Powers Mobile AI: Inside Alibaba’s Open‑Source Inference Engine

Alibaba’s MNN (Mobile Neural Network) engine, now open‑sourced on GitHub, showcases how a lightweight, end‑side deep‑learning inference framework tackles fragmentation, optimizes model conversion, scheduling, and execution across diverse devices, delivering significant performance gains for mobile and IoT AI applications.

MNNModel OptimizationOperator fusion
0 likes · 15 min read
How MNN Powers Mobile AI: Inside Alibaba’s Open‑Source Inference Engine
Amap Tech
Amap Tech
Jun 5, 2019 · Artificial Intelligence

Applying Learning to Rank for Search Suggestion Optimization at Gaode Maps

Gaode Maps applied Learning to Rank to optimize search suggestions, moving from rule-based to gradient boosted rank model, addressing sample construction and feature sparsity via session-based labeling and loss adjustment, achieving a seven‑point MRR gain and higher coverage, and paving the way for personalization and deep learning.

Gaode MapsLearning-to-RankModel Optimization
0 likes · 11 min read
Applying Learning to Rank for Search Suggestion Optimization at Gaode Maps
iQIYI Technical Product Team
iQIYI Technical Product Team
May 30, 2019 · Mobile Development

SmileAR: iQIYI’s Mobile AR Solution Powered by TensorFlow Lite

SmileAR, iQIYI’s self‑developed mobile AR platform powered by TensorFlow Lite, delivers real‑time face, body and gesture recognition across iQIYI’s apps through MobileNet‑based models, quantization‑aware training, multi‑task learning and encrypted SDKs, achieving fast, lightweight, cross‑platform AR experiences for millions of users.

ARCross-PlatformModel Optimization
0 likes · 10 min read
SmileAR: iQIYI’s Mobile AR Solution Powered by TensorFlow Lite
58 Tech
58 Tech
Nov 9, 2018 · Artificial Intelligence

Search List Ranking Efficiency Optimization Practices at 58.com

This article details how 58.com improved the efficiency of its search list ranking by moving from simple time‑based ordering to a comprehensive ranking framework that incorporates feedback strategies, basic machine‑learning models, feature upgrades, and advanced model upgrades, achieving significant gains in click‑through, conversion, and revenue across multiple business lines.

Model OptimizationOnline Advertisingclick-through rate
0 likes · 23 min read
Search List Ranking Efficiency Optimization Practices at 58.com
High Availability Architecture
High Availability Architecture
Jul 12, 2017 · Artificial Intelligence

Machine Learning Platform and Risk‑Control Applications at DianRong Net

The article presents a comprehensive overview of DianRong Net's in‑house machine‑learning platform built on Spark, its workflow, pain points it addresses, risk‑control case studies using graph mining, and practical tips for improving model performance through data, algorithms, hyper‑parameter tuning and ensemble methods.

Big DataModel OptimizationSpark
0 likes · 14 min read
Machine Learning Platform and Risk‑Control Applications at DianRong Net
Meituan Technology Team
Meituan Technology Team
May 5, 2017 · Artificial Intelligence

Four Essential Elements for Advancing Machine Learning Projects: Model, Data, Features, and Business

Advancing a machine‑learning project requires focusing first on the core business problem, then designing comprehensive features, ensuring high‑quality data, and finally selecting an appropriate model, because business impact drives success while features and data set the performance ceiling and model choice balances accuracy with interpretability.

Model Optimizationbusiness alignmentdata science
0 likes · 13 min read
Four Essential Elements for Advancing Machine Learning Projects: Model, Data, Features, and Business