在主动学习中，训练数据集的大小和复杂性随时间而变化。随着更多要点，由主动学习开始时可用的数据量良好的简单模型可能会受到偏见的影响。可能非常适合于完整数据集的灵活模型可能会在积极学习开始时受到过度装备。我们使用深度不确定性网络（DUNS）来解决这个问题，其中一个BNN变体，其中网络的深度以及其复杂性。我们发现DUNS在几个活跃的学习任务上表现出其他BNN变体。重要的是，我们表明，在DUNs表现最佳的任务上，它们呈现出比基线的显着不太容易。

Farquhar等人。[2021]表明，纠正具有协议式模型的主动学习偏差导致下游性能提高。然而，对于诸如NNS的过度指数化模型，然而，校正导致减少或不变的性能。他们认为这是由于“过度装备偏见”，其抵消了主动学习偏差。我们表明深度不确定性网络在低过度装备制度中运行，就像顺相位表一样。因此，它们应该看到具有偏差校正的性能的增加。令人惊讶的是，他们没有。我们建议这种负面结果以及结果Farquhar等。[2021]可以通过普遍化误差的偏差差异分解的镜头来解释。

在Mackay（1992）上展开，我们认为，用于主动学习的基于模式的方法 - 类似的基于模型 - 如秃顶 - 具有基本的缺点：它们未直接解释输入变量的测试时间分布。这可以导致采集策略中的病理，因为模型参数的最大信息是最大信息，可能不是最大地信息，例如，当池集中的数据比最终预测任务的数据更大时，或者池和试验样品的分布不同。为了纠正这一点，我们重新审视了基于最大化关于可能的未来预测的预期信息的收购策略，参考这是预期的预测信息增益（EPIG）。由于EPIG对批量采集不扩展，我们进一步检查了替代策略，秃头和EPIG之间的混合，我们称之为联合预测信息增益（Jepig）。我们考虑在各种数据集中使用贝叶斯神经网络的主动学习，检查池集中分布班下的行为。

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).

主动学习是减少训练深神经网络模型中数据量的流行方法。它的成功取决于选择有效的采集函数，该功能尚未根据其预期的信息进行排名。在不确定性抽样中，当前模型具有关于点类标签的不确定性是这种类型排名的主要标准。本文提出了一种在培训卷积神经网络（CNN）中进行不确定性采样的新方法。主要思想是使用CNN提取提取的特征表示作为培训总产品网络（SPN）的数据。由于SPN通常用于估计数据集的分布，因此它们非常适合估算类概率的任务，这些概率可以直接由标准采集函数（例如最大熵和变异比率）使用。此外，我们通过在SPN模型的帮助下通过权重增强了这些采集函数。这些权重使采集功能对数据点的可疑类标签的多样性更加敏感。我们的方法的有效性在对MNIST，时尚持续和CIFAR-10数据集的实验研究中得到了证明，我们将其与最先进的方法MC辍学和贝叶斯批次进行了比较。

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points jointly for the task of deep Bayesian active learning. BatchBALD is a greedy linear-time 1 − 1 /e-approximate algorithm amenable to dynamic programming and efficient caching. We compare BatchBALD to the commonly used approach for batch data acquisition and find that the current approach acquires similar and redundant points, sometimes performing worse than randomly acquiring data. We finish by showing that, using BatchBALD to consider dependencies within an acquisition batch, we achieve new state of the art performance on standard benchmarks, providing substantial data efficiency improvements in batch acquisition.

Large multilayer neural networks trained with backpropagation have recently achieved state-ofthe-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights.

神经线性模型（NLM）是深度贝叶斯模型，通过从数据中学习特征，然后对这些特征进行贝叶斯线性回归来产生预测的不确定性。尽管他们受欢迎，但很少有作品专注于有条理地评估这些模型的预测性不确定性。在这项工作中，我们证明了NLMS的传统培训程序急剧低估了分发输入的不确定性，因此它们不能在风险敏感的应用中暂时部署。我们确定了这种行为的基本原因，并提出了一种新的培训框架，捕获下游任务的有用预测不确定性。

现代深度学习方法构成了令人难以置信的强大工具，以解决无数的挑战问题。然而，由于深度学习方法作为黑匣子运作，因此与其预测相关的不确定性往往是挑战量化。贝叶斯统计数据提供了一种形式主义来理解和量化与深度神经网络预测相关的不确定性。本教程概述了相关文献和完整的工具集，用于设计，实施，列车，使用和评估贝叶斯神经网络，即使用贝叶斯方法培训的随机人工神经网络。

贝叶斯范式有可能解决深度神经网络的核心问题，如校准和数据效率低差。唉，缩放贝叶斯推理到大量的空间通常需要限制近似。在这项工作中，我们表明它足以通过模型权重的小子集进行推动，以便获得准确的预测后断。另一个权重被保存为点估计。该子网推断框架使我们能够在这些子集上使用表现力，否则难以相容的后近近似。特别是，我们将子网线性化LAPLACE作为一种简单，可扩展的贝叶斯深度学习方法：我们首先使用线性化的拉普拉斯近似来获得所有重量的地图估计，然后在子网上推断出全协方差高斯后面。我们提出了一个子网选择策略，旨在最大限度地保护模型的预测性不确定性。经验上，我们的方法对整个网络的集合和较少的表达后近似进行了比较。

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning.

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning.

用于估计模型不确定性的线性拉普拉斯方法在贝叶斯深度学习社区中引起了人们的重新关注。该方法提供了可靠的误差线，并接受模型证据的封闭式表达式，从而可以选择模型超参数。在这项工作中，我们检查了这种方法背后的假设，尤其是与模型选择结合在一起。我们表明，这些与一些深度学习的标准工具（构成近似方法和归一化层）相互作用，并为如何更好地适应这种经典方法对现代环境提出建议。我们为我们的建议提供理论支持，并在MLP，经典CNN，具有正常化层，生成性自动编码器和变压器的剩余网络上进行经验验证它们。

We investigate the efficacy of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard construction may be unnecessary. To this end, we prove that expressive predictive distributions require only small amounts of stochasticity. In particular, partially stochastic networks with only $n$ stochastic biases are universal probabilistic predictors for $n$-dimensional predictive problems. In empirical investigations, we find no systematic benefit of full stochasticity across four different inference modalities and eight datasets; partially stochastic networks can match and sometimes even outperform fully stochastic networks, despite their reduced memory costs.

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.

We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic gradients for variational Bayesian inference (SGVB) of a posterior over model parameters, while retaining parallelizability. This local reparameterization translates uncertainty about global parameters into local noise that is independent across datapoints in the minibatch. Such parameterizations can be trivially parallelized and have variance that is inversely proportional to the minibatch size, generally leading to much faster convergence. Additionally, we explore a connection with dropout: Gaussian dropout objectives correspond to SGVB with local reparameterization, a scale-invariant prior and proportionally fixed posterior variance. Our method allows inference of more flexibly parameterized posteriors; specifically, we propose variational dropout, a generalization of Gaussian dropout where the dropout rates are learned, often leading to better models. The method is demonstrated through several experiments.

在这项工作中，我们使用变分推论来量化无线电星系分类的深度学习模型预测的不确定性程度。我们表明，当标记无线电星系时，个体测试样本的模型后差水平与人类不确定性相关。我们探讨了各种不同重量前沿的模型性能和不确定性校准，并表明稀疏事先产生更良好的校准不确定性估计。使用单个重量的后部分布，我们表明我们可以通过从最低信噪比（SNR）中除去权重来修剪30％的完全连接的层权重，而无需显着损失性能。我们证明，可以使用基于Fisher信息的排名来实现更大程度的修剪，但我们注意到两种修剪方法都会影响Failaroff-Riley I型和II型无线电星系的不确定性校准。最后，我们表明，与此领域的其他工作相比，我们经历了冷的后效，因此后部必须缩小后加权以实现良好的预测性能。我们检查是否调整成本函数以适应模型拼盘可以弥补此效果，但发现它不会产生显着差异。我们还研究了原则数据增强的效果，并发现这改善了基线，而且还没有弥补观察到的效果。我们将其解释为寒冷的后效，因为我们的培训样本过于有效的策划导致可能性拼盘，并将其提高到未来无线电银行分类的潜在问题。

Acquiring labeled data is challenging in many machine learning applications with limited budgets. Active learning gives a procedure to select the most informative data points and improve data efficiency by reducing the cost of labeling. The info-max learning principle maximizing mutual information such as BALD has been successful and widely adapted in various active learning applications. However, this pool-based specific objective inherently introduces a redundant selection and further requires a high computational cost for batch selection. In this paper, we design and propose a new uncertainty measure, Balanced Entropy Acquisition (BalEntAcq), which captures the information balance between the uncertainty of underlying softmax probability and the label variable. To do this, we approximate each marginal distribution by Beta distribution. Beta approximation enables us to formulate BalEntAcq as a ratio between an augmented entropy and the marginalized joint entropy. The closed-form expression of BalEntAcq facilitates parallelization by estimating two parameters in each marginal Beta distribution. BalEntAcq is a purely standalone measure without requiring any relational computations with other data points. Nevertheless, BalEntAcq captures a well-diversified selection near the decision boundary with a margin, unlike other existing uncertainty measures such as BALD, Entropy, or Mean Standard Deviation (MeanSD). Finally, we demonstrate that our balanced entropy learning principle with BalEntAcq consistently outperforms well-known linearly scalable active learning methods, including a recently proposed PowerBALD, a simple but diversified version of BALD, by showing experimental results obtained from MNIST, CIFAR-100, SVHN, and TinyImageNet datasets.

主动学习在许多领域中展示了数据效率。现有的主动学习算法，特别是在深贝叶斯活动模型的背景下，严重依赖模型的不确定性估计的质量。然而，这种不确定性估计可能会严重偏见，特别是有限和不平衡的培训数据。在本文中，我们建议平衡，贝叶斯深度活跃的学习框架，减轻这种偏差的影响。具体地，平衡采用了一种新的采集功能，该函数利用了等效假设类别捕获的结构，并促进了不同的等价类别之间的分化。直观地，每个等价类包括具有类似预测的深层模型的实例化，并且平衡适应地将等同类的大小调整为学习进展。除了完整顺序设置之外，我们还提出批量平衡 - 顺序算法的泛化算法到批量设置 - 有效地选择批次的培训实施例，这些培训实施例是对模型改进的联合有效的培训实施例。我们展示批量平衡在多个基准数据集上实现了最先进的性能，用于主动学习，并且这两个算法都可以有效地处理通常涉及多级和不平衡数据的逼真挑战。

Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNsextracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and nonlinearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.