model ensemble | BestHub

AntTech

Jun 21, 2022 · Artificial Intelligence

FinQA Competition Winning Model by Ant Risk AI: Architecture, Dataset, and Experimental Results

Ant Risk AI’s team secured the FinQA competition champion by presenting a comprehensive model that combines a retriever and program generator, detailed dataset analysis, domain-specific language design, and extensive experiments demonstrating superior execution and program accuracy on financial numerical reasoning tasks.

Dataset AnalysisFinQANLP

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FinQA Competition Winning Model by Ant Risk AI: Architecture, Dataset, and Experimental Results

DataFunTalk

Nov 2, 2021 · Artificial Intelligence

Personalized Recommendation and Advertising Algorithms for E‑commerce: Business Overview, Recall and Ranking Optimization, Multi‑Task Modeling, and Future Directions

This article presents a comprehensive technical overview of JD.com’s e‑commerce recommendation and advertising systems, covering business scenarios, recall optimizations (profile and similarity‑based), multi‑task ranking improvements, sample weighting, multi‑model ensembles, PID‑based CPC control, conversion‑delay modeling, and the achieved performance gains and future research plans.

CTR optimizationRecommendation systemsadvertising

0 likes · 18 min read

Personalized Recommendation and Advertising Algorithms for E‑commerce: Business Overview, Recall and Ranking Optimization, Multi‑Task Modeling, and Future Directions

58 Tech

Jun 16, 2021 · Artificial Intelligence

Improving Text Matching Accuracy in Voice Assistants: Experiments with Siamese Networks, BERT Models, and Advanced Tricks

This article evaluates classic Siamese networks, various BERT‑based pretrained models, and several training tricks such as adversarial training, k‑fold cross‑validation, and model ensembling on both a public similarity‑sentence competition dataset and an internal voice‑assistant standard question matching dataset, ultimately raising accuracy from 97.23 % to 99.5 %.

BERTSiamese networkText Matching

0 likes · 15 min read

Improving Text Matching Accuracy in Voice Assistants: Experiments with Siamese Networks, BERT Models, and Advanced Tricks

Sohu Tech Products

Jun 17, 2020 · Artificial Intelligence

Ensemble Learning: Concepts, Methods, and Applications in Deep Learning

This article provides a comprehensive overview of ensemble learning, explaining its principles, common classifiers, major ensemble strategies such as bagging, boosting, and stacking, and demonstrates practical deep‑learning ensemble techniques like Dropout, test‑time augmentation, and Snapshot ensembles with code examples.

BoostingDeep Learningbagging

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Ensemble Learning: Concepts, Methods, and Applications in Deep Learning

iQIYI Technical Product Team

Jul 12, 2019 · Artificial Intelligence

Multimodal Video Retrieval Solution for iQIYI Challenge: Feature Fusion and Model Ensemble

The ‘One Name’ team from Nanjing University achieved a MAP of 0.8986 and third place in the iQIYI multimodal video retrieval challenge by fusing official face embeddings with scene features, using channel‑attention‑based video feature fusion, a multimodal SE‑ResNeXt module, and a carefully partitioned model ensemble.

deep learningfeature fusioniQIYI challenge

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Multimodal Video Retrieval Solution for iQIYI Challenge: Feature Fusion and Model Ensemble

iQIYI Technical Product Team

Jun 28, 2019 · Artificial Intelligence

Watchdog Team's TOP1 Solution for the iQIYI & ACMMM2019 Multimodal Video Person Recognition Challenge

Watchdog team won TOP1 in iQIYI & ACMMM2019 multimodal video person recognition challenge using pre‑extracted multimodal features, a 2048‑dim classifier with BCE loss, re‑ranking, DALI‑accelerated re‑detection, fine‑tuned InsightFace, and multi‑model ensembling achieving ~91% test accuracy.

Deep Learningfeature fusionmodel ensemble

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Watchdog Team's TOP1 Solution for the iQIYI & ACMMM2019 Multimodal Video Person Recognition Challenge

Tencent Advertising Technology

Jun 13, 2019 · Artificial Intelligence

Competition Solution Overview: Data Analysis, Rule‑Based and Neural Network Models for Advertising Prediction

The article details a contestant's end‑to‑end approach for an advertising competition, covering data analysis, rule‑based preprocessing, a three‑layer neural network architecture, model‑rule ensemble weighting, self‑correction strategies for the B phase, and final model‑only solutions that achieved top scores.

Feature EngineeringNeural Networkadvertising

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Competition Solution Overview: Data Analysis, Rule‑Based and Neural Network Models for Advertising Prediction

Ctrip Technology

Nov 21, 2018 · Artificial Intelligence

Algorithmic Strategies and Insights from Ctrip Hotel Ranking Team’s Participation in the 2018 ACM WSDM and RecSys Challenges

This article details the Ctrip Hotel ranking team's feature‑engineering and model‑innovation approaches—including session features, cold‑start mitigation, discriminative re‑weighting, and ensemble methods—that secured Top‑5 placements in the 2018 ACM WSDM and RecSys recommendation system competitions.

Cold StartFeature EngineeringRecommendation systems

0 likes · 12 min read

Algorithmic Strategies and Insights from Ctrip Hotel Ranking Team’s Participation in the 2018 ACM WSDM and RecSys Challenges

Tencent Advertising Technology

Apr 3, 2018 · Artificial Intelligence

Runner‑up Team’s Experience and Practical Tips from the First Tencent Social Advertising University Algorithm Competition

The article shares the runner‑up team’s reflections on the first Tencent Social Advertising university algorithm contest, covering data splitting, feature engineering, handling large datasets, model selection, ensemble techniques, and final advice to help future participants succeed in conversion‑rate prediction challenges.

advertisingcompetitionconversion rate prediction

0 likes · 7 min read

Runner‑up Team’s Experience and Practical Tips from the First Tencent Social Advertising University Algorithm Competition

Tencent Advertising Technology

May 9, 2017 · Artificial Intelligence

Kaggle Competition Overview and Practical Guide for Data Mining

This article provides a comprehensive introduction to Kaggle, covering its history, competition formats, participation rules, public and private leaderboard mechanics, and a step‑by‑step workflow that includes data analysis, cleaning, feature engineering, model training, validation, hyper‑parameter tuning, ensemble techniques, and automation frameworks for successful data‑mining contests.

Kagglemodel ensemble

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Kaggle Competition Overview and Practical Guide for Data Mining