Ensemble Learning
310 papers with code • 1 benchmarks • 3 datasets
Libraries
Use these libraries to find Ensemble Learning models and implementationsDatasets
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
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
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
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
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
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.