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Ensemble Learning to Improve Machine Learning Results

#artificialintelligence

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, i.e. learners of the same type, leading to homogeneous ensembles. There are also some methods that use heterogeneous learners, i.e. learners of different types, leading to heterogeneous ensembles. In order for ensemble methods to be more accurate than any of its individual members, the base learners have to be as accurate as possible and as diverse as possible. Bagging stands for bootstrap aggregation.


BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

arXiv.org Machine Learning

Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the number of networks, which quickly becomes untenable. In this paper, we propose BatchEnsemble 1, an ensemble method whose computational and memory costs are significantly lower than typical ensembles. BatchEnsemble achieves this by defining each weight matrix to be the Hadamard product of a shared weight among all ensemble members and a rank-one matrix per member. Unlike ensembles, BatchEnsemble is not only parallelizable across devices, where one device trains one member, but also parallelizable within a device, where multiple ensemble members are updated simultaneously for a given mini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and out-of-distribution tasks, BatchEnsemble yields competitive accuracy and uncertainties as typical ensembles; the speedup at test time is 3X and memory reduction is 3X at an ensemble of size 4. We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs. We further show that BatchEnsemble can easily scale up to lifelong learning on Split-ImageNet which involves 100 sequential learning tasks.


[Report] Imprinting and recalling cortical ensembles

Science

Donald Hebb's hypothesis that coactivation of neurons leads to the formation of ensembles of neurons has inspired neuroscientists for decades. The experimental creation of such ensembles has been technically challenging. Using two-photon optogenetic stimulation with single-cell resolution, Carrillo-Reid et al. discovered that recurrent activation of a group of neurons creates an ensemble that is imprinted in the brain circuitry. Activation of a single neuron can lead to recall of the entire ensemble in a phenomenon called pattern completion. The artificial ensemble persists over days and can be reactivated at later time points without interfering with endogenous circuitry.


Growing and Pruning Ensembles in Python

#artificialintelligence

Ensemble member selection refers to algorithms that optimize the composition of an ensemble. This may involve growing an ensemble from available models or pruning members from a fully defined ensemble. The goal is often to reduce the model or computational complexity of an ensemble with little or no effect on the performance of an ensemble, and in some cases find a combination of ensemble members that results in better performance than blindly using all contributing models directly. In this tutorial, you will discover how to develop ensemble selection algorithms from scratch. Growing and Pruning Ensembles in Python Photo by FaBio C, some rights reserved.


Ensemble Pruning based on Objection Maximization with a General Distributed Framework

arXiv.org Artificial Intelligence

Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method including a centralized version and a distributed version, in which the latter is to speed up the former's execution. At last, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of existing ensemble pruning methods and achieve less time consuming without much accuracy decline. Experimental results validate the efficiency of our framework and methods, particularly with regard to a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.