Ensemble Learning

237 papers with code • 1 benchmarks • 3 datasets

This task has no description! Would you like to contribute one?

Libraries

Use these libraries to find Ensemble Learning models and implementations

Most implemented papers

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

Adlik/model_optimizer 17 Sep 2019

Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements.

Sample Efficient Ensemble Learning with Catalyst.RL

Scitator/run-skeleton-run-in-3d 29 Mar 2020

We present Catalyst. RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research.

Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

YunpengZhai/MEB-Net ECCV 2020

Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.

Conformal prediction interval for dynamic time-series

hamrel-cxu/EnbPI 18 Oct 2020

We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.

Using Transformer based Ensemble Learning to classify Scientific Articles

SDPRA-2021/shared-task 19 Feb 2021

The first one is a RoBERTa [10] based model built over these abstracts.

Domain Generalization: A Survey

KaiyangZhou/Dassl.pytorch 3 Mar 2021

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce.

Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning

smallcube/EAL-GAN 24 Apr 2021

In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data.

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

vita-group/freetickets ICLR 2022

Our framework, FreeTickets, is defined as the ensemble of these relatively cheap sparse subnetworks.

Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

Mai-CS/enhanced-deep-convolutional-forest 10 Oct 2021

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords.

A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews

miladvazan/Joint_Learning_Classification_Persian_Reviews- 17 Jan 2022

The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.