Efficient Diversity-Driven Ensemble for Deep Neural Networks
Explore this paper's citation graph
- Type
- preprint
- Published
- 2020-04-01
- Cited by
- 27
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W3031900123
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:218907028
Keywords
Boosting (machine learning), Computer science, Ensemble learning, Artificial intelligence, Machine learning
References
- A fast and efficient pre-training method based on layer-by-layer maximum discrimination for deep neural networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Distilling the Knowledge in a Neural Network
- Convolutional Neural Networks for Sentence Classification
- A Preliminary Study on Negative Correlation Learning via Correlation-Corrected Data (NCCD)
- A decision-theoretic generalization of on-line learning and an application to boosting
- Error Correlation and Error Reduction in Ensemble Classifiers
- Neural network ensembles: evaluation of aggregation algorithms
- Optimal ensemble averaging of neural networks
- The Elements of Statistical Learning
- Neural Networks and the Bias/Variance Dilemma
- Learning Word Vectors for Sentiment Analysis
- A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
- Simultaneous training of negatively correlated neural networks in an ensemble
- Improving Performance in Neural Networks Using a Boosting Algorithm
- An analysis of diversity measures
- Ensemble-based classifiers
- Negative correlation learning for classification ensembles
- A cooperative ensemble learning system
- A Survey on Transfer Learning
Cited by
- ROD: Reception-aware Online Distillation for Sparse Graphs
- Efficient Model Store and Reuse in an OLML Database System
- Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles
- ICDE 2020 TOC
- Deep Combinatorial Aggregation
- DivBO: Diversity-aware CASH for Ensemble Learning
- Lightweight multi-scale classification of chest radiographs via size-specific batch normalization
- Transfer learning for ensembles: reducing computation time and keeping the diversity
- LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation
- A geometric framework for multiclass ensemble classifiers
- A Two-Phase Recall-and-Select Framework for Fast Model Selection
- CASH via Optimal Diversity for Ensemble Learning
- Deep negative correlation classification
- Boosting Deep Ensembles with Learning Rate Tuning
- Similarity of Neural Network Models: A Survey of Functional and Representational Measures
- Diversity Learning by Minimizing Construction Error of Ensemble Loss for Bayesian Optimization
- Representational Difference Explanations
- Improving Generalization of End-to-End ASR through Diversity and Independence Regularization
- Auto-CEn: AutoML for Classifier Ensembles - Diversity-Based Classifier Selection and Decision Fusion Optimization
- Representational Similarity via Interpretable Visual Concepts
Related papers
- Bagged ensembles with tunable parameters
- Ensemble Techniques to improve the performance of the High Dimensional MultiClass Algorithms
- A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
- Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
- Performance analysis of ensemble learning for predicting defects in open source software
- Data mining using filtering approaches and ensemble methods
- CNN Ensemble Learning Method for Transfer learning: A Review