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ensemble

7 articles · Page 1 of 1
DeepHub IMBA
DeepHub IMBA
Aug 5, 2026 · Artificial Intelligence

Designing Multi‑Model Systems: 4 Common Architecture Patterns and When to Use Them

The article examines multi‑model system design, presenting four core architecture patterns—sequential (pipeline, router), parallel (fan‑out, voting), hierarchical (planner‑executor, supervisor‑worker), and ensemble (weighted, consensus)—and discusses their trade‑offs, implementation details, and scenarios where multi‑model approaches add real value.

AIarchitecture patternsensemble
0 likes · 11 min read
Designing Multi‑Model Systems: 4 Common Architecture Patterns and When to Use Them
AI Agent Research Hub
AI Agent Research Hub
Jun 19, 2026 · Artificial Intelligence

DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube

This tutorial presents a complete DeepONet workflow—two‑step separated training, Rowdy activation, SVD orthogonalisation, and a 10‑member ensemble—that predicts the density, velocity and pressure fields of the one‑dimensional Sod shock‑tube problem with an average test‑set relative error of 2.23% after only 22 minutes of training on an RTX 4090.

DeepONetJAXNeural Operator
0 likes · 22 min read
DeepONet Neural Operator for Fast Prediction of Non‑Smooth Discontinuities in the Sod Shock Tube
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 15, 2026 · Artificial Intelligence

Is RL Dead in LLM Post-Training? MIT’s RandOpt Challenges Traditional Methods

The MIT‑CSAIL paper introduces RandOpt, a single‑step, gradient‑free, fully parallel post‑training algorithm that adds Gaussian noise to pretrained LLM weights and ensembles the results, achieving or surpassing PPO/GRPO performance by exploiting dense "neural thickets" that emerge as model scale grows.

LLMRandOptensemble
0 likes · 12 min read
Is RL Dead in LLM Post-Training? MIT’s RandOpt Challenges Traditional Methods
Meituan Technology Team
Meituan Technology Team
Jun 9, 2022 · Artificial Intelligence

FSL++: A Few-Shot Learning Model for Chinese Language Understanding that Tops the FewCLUE Benchmark

FSL++—a RoBERTa‑large‑based few‑shot model enhanced with domain‑adaptive pre‑training, prompt learning, diverse embedding‑level augmentations, and ensemble self‑training—topped the Chinese FewCLUE benchmark, beating human accuracy on news and scientific classification tasks and delivering measurable gains across multiple Meituan product scenarios.

Chinese language understandingData AugmentationNLP
0 likes · 23 min read
FSL++: A Few-Shot Learning Model for Chinese Language Understanding that Tops the FewCLUE Benchmark
Xianyu Technology
Xianyu Technology
May 10, 2018 · Artificial Intelligence

Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models

By converting Mercari’s product titles, descriptions, and categorical data into TF‑IDF vectors and embeddings, training MLP and GRU networks, and ensembling them with weighted averaging, the authors achieve a 0.3873 RMSLE—matching the competition’s top score—and demonstrate the power of text‑only price prediction for C2C marketplaces.

GRUTFIDFe-commerce
0 likes · 8 min read
Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models
21CTO
21CTO
Sep 20, 2017 · Big Data

Winning O2O Coupon Redemption with XGBoost, GBDT, and Feature Engineering

This article details a data-driven solution for the 2016 O2O coupon redemption competition, describing dataset partitioning, extensive feature engineering across user, merchant, and coupon dimensions, handling leakage, and model fusion using XGBoost, GBDT, and RandomForest, achieving top AUC scores through weighted ensemble.

GBDTXGBoostcoupon redemption
0 likes · 12 min read
Winning O2O Coupon Redemption with XGBoost, GBDT, and Feature Engineering