Ensemble Learning
482 papers
Papers per year
1
2
2
3
5
10
4
6
5
11
4
12
15
7
6
16
21
30
35
33
57
52
49
60
36
2000
'5
'10
'15
'20
'25
Papers
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising
IJCNLP 2017
Confident Multiple Choice Learning
ICML 2017
Exclusivity Regularized Machine: A New Ensemble SVM Classifier
IJCAI 2017
Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features
CVPR 2017
Forest-type Regression with General Losses and Robust Forest
ICML 2017
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
JMLR 2017
Learning and Applying Case Adaptation Rules for Classification: An Ensemble Approach
IJCAI 2017
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
NeurIPS 2017
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction
IJCNLP 2017
Multi-Target Ensemble Learning for Monaural Speech Separation
Interspeech 2017
SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
SemEval 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
NeurIPS 2017
TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
SemEval 2017
What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
CVPR 2017
Logistic Boosting Regression for Label Distribution Learning
CVPR 2016
mlr: Machine Learning in R
JMLR 2016
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
JMLR 2016
Sequence Student-Teacher Training of Deep Neural Networks
Interspeech 2016
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
NeurIPS 2016
Swapout: Learning an ensemble of deep architectures
NeurIPS 2016
A Randomized Ensemble Approach to Industrial CT Segmentation
ICCV 2015
DART: Dropouts meet Multiple Additive Regression Trees
AISTATS 2015
Efficient and Robust Automated Machine Learning
NeurIPS 2015
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables
ICCV 2015
Learning to Propose Objects
CVPR 2015