Ensemble Learning
310 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Ensemble Learning
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Libraries
Use these libraries to find Ensemble Learning models and implementationsDatasets
SMS Spam Collection Data Set
Wild-Time
Most implemented papers
Gossip Learning with Linear Models on Fully Distributed Data
Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications.
Predicting the direction of stock market prices using random forest
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.
Masksembles for Uncertainty Estimation
nikitadurasov/masksembles
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CVPR 2021
Our central intuition is that there is a continuous spectrum of ensemble-like models of which MC-Dropout and Deep Ensembles are extreme examples.
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
namisan/mt-dnn
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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.
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
boun-tabi/debiaseddta-reproduce
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Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.
Representation Topology Divergence: A Method for Comparing Neural Network Representations
ilyatrofimov/rtd
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Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet.
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
scikit-learn-contrib/imbalanced-learn
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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
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
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods.
