user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Paper content:
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.
In this paper, we propose BatchEnsemble, 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 contextual bandits 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.
Ensembling is one of the oldest tricks in machine learning literature (Hansen & Salamon, 1990) .
By combining the outputs of several models, an ensemble can achieve better performance than any of its members.
Many researchers demonstrate that a good ensemble is one where the ensemble's members are both accurate and make independent errors (Perrone & Cooper, 1992; Maclin & Opitz, 1999) .
In neural networks, SGD (Bottou, 2003) and its variants (Kingma & Ba, 2014) are the most common optimization algorithm.
The random noise from sampling mini-batches of data in SGD-like algorithms and random initialization of the deep neural networks, combined with the fact that there is a wide variety of local minima solutions in high dimensional optimization problem (Kawaguchi, 2016; Ge et al., 2015) , results in the following observation: deep neural networks trained with different random seeds can converge to very different local minima although they share similar error rates.
One of the consequence is that neural networks trained with different random seeds will usually not make all the same errors on the test set, i.e. they may disagree on a prediction given the same input even if the model has converged.
Ensembles of neural networks benefit from the above observation to achieve better performance by averaging or majority voting on the output of each ensemble member (Xie et al., 2013; Huang et al., 2017) .
It is shown that ensembles of models perform at least as well as its individual members and diverse ensemble members lead to better performance (Krogh & Vedelsby, 1995) .
More recently, Lakshminarayanan et al. (2017) showed that deep ensembles give reliable predictive uncertainty estimates while remaining simple and scalable.
A further study confirms that deep ensembles generally achieves the best performance on out-of-distribution uncertainty benchmarks (Ovadia et al., 2019) compared to other methods such as MC-dropout (Gal & Ghahramani, 2015) .
Despite their success on benchmarks, ensembles in practice are limited due to their expensive computational and memory costs, which increase linearly with the ensemble size in both training and testing.
Computation-wise, each ensemble member requires a separate neural network forward pass of its inputs.
Memory-wise, each ensemble member requires an independent copy of neural network weights, each up to millions (sometimes billions) of parameters.
This memory requirement also makes many tasks beyond supervised learning prohibitive.
For example, in lifelong learning, a natural idea is to use a separate ensemble member for each task, adaptively growing the total number of parameters by creating a new independent set of weights for each new task.
No previous work achieves competitive performance on lifelong learning via ensemble methods, as memory is a major bottleneck.
Our contribution: In this paper, we aim to address the computational and memory bottleneck by building a more parameter efficient ensemble model: BatchEnsemble.
We achieve this goal by exploiting a novel ensemble weight generation mechanism: the weight of each ensemble member is generated by the Hadamard product between:
a. one shared weight among all ensemble members.
b. one rank-one matrix that varies among all members, which we refer to as fast weight in the following sections.
Figure 1 compares testing and memory cost between BatchEnsemble and naive ensemble.
Unlike typical ensembles, BatchEnsemble is mini-batch friendly, where it is not only parallelizable across devices like typical ensembles but also parallelizable within a device.
Moreover, it incurs only minor memory overhead because a large number of weights are shared across ensemble members.
Empirically, we show that BatchEnsemble has the best trade-off among accuracy, running time, and memory on several deep learning architectures and learning tasks: CIFAR-10/100 classification with ResNet32 (He et al., 2016) and WMT14 EN-DE/EN-FR machine translation with Transformer (Vaswani et al., 2017) .
Additionally, we show that BatchEnsemble is also effective in uncertainty evaluation on contextual bandits.
Finally, we show that BatchEnsemble can be successfully applied in lifelong learning and scale up to 100 sequential learning tasks without catastrophic forgetting and the need of memory buffer.
We introduced BatchEnsemble, an efficient method for ensembling and lifelong learning.
BatchEnsemble can be used to improve the accuracy and uncertainty of any neural network like typical ensemble methods.
More importantly, BatchEnsemble removes the computation and memory bottleneck of typical ensemble methods, enabling its successful application to not only faster ensembles but also lifelong learning on up to 100 tasks.
We believe BatchEnsemble has great potential to improve in lifelong learning.
Our work may serve as a starting point for a new research area.

assistant: text: We introduced BatchEnsemble, an efficient method for ensembling and lifelong learning which can be used to improve the accuracy and uncertainty of any neural network like typical ensemble methods.