Ensemble Learning

141 papers with code • 0 benchmarks • 1 datasets

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Use these libraries to find Ensemble Learning models and implementations

Most implemented papers

Predicting the direction of stock market prices using random forest

jmartinezheras/reproduce-stock-market-direction-random-forests 29 Apr 2016

In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning.

Gossip Learning with Linear Models on Fully Distributed Data

tribler/trustchain-superapp 7 Sep 2011

Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications.

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

scikit-learn-contrib/imbalanced-learn 21 Sep 2016

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition.

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

cpapadimitriou/Click-Through-Rate-prediction 4 Nov 2017

In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings.

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

learn-ensemble/PY-SUMMA 13 Feb 2018

Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods.

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

benedekrozemberczki/karateclub ASONAM 2019 2019

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

General audio tagging with ensembling convolutional neural network and statistical features

Cocoxili/DCASE2018Task2 30 Oct 2018

Audio tagging is challenging due to the limited size of data and noisy labels.

Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding

namisan/mt-dnn 20 Apr 2019

This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks.

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.