deep learning ensembles

Ensemble modeling is the process by which a machine learning model combines distinct base models to generate generalized predictions using a combination of the predictive power of each of its components. Such a dynamic approach thereby effectively addresses the tradeoff between prediction accuracy and prediction earliness. (eds.) Deep Ensembles: A Loss Landscape Perspective This can be due to a false negative prediction (i.e., a non-violation is predicted despite an actual violation), or because the reliability threshold was not reached, even though it was an actual violation. Springer, Cham (2015). Similarly, Leontjeva et al. To be precise, a prior distribution is specified for each weight and bias. Each base model differs with respect to the variable elements i.e. Each missed required adaptation means one less opportunity for preventing or mitigating a problem. Metzger, A., Neubauer, A., Bohn, P., Pohl, K. (2019). (eds.) (eds.) Deep Learning: A Comprehensive Overview on Techniques - Springer Bagging generates m new training data sets from the whole training set by sampling from the whole training data set uniformly and with replacement. We just have to take care not to arbitrarily divide the literature. Cited by(0), Figures(6) / Tables(8), Department of ICT Convergence, Soonchunhyang University, Asan 31538, Korea, Department of Ophthalmology, Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea, Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea, 2023 the Author(s), licensee AIMS Press. https://doi.org/10.1007/978-3-319-45348-4_23, Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M. https://doi.org/10.1007/978-3-319-69035-3_25, Metzger, A., Fcker, F.: Predictive business process monitoring considering reliability estimates. International Conference on Advanced Information Systems Engineering, CAiSE 2019: Advanced Information Systems Engineering Experimental results based on four real-world data sets suggests that our dynamic approach offers cost savings of 27% on average when compared to using a fixed, static prediction point. Yet, probabilities estimated by most of these prediction techniques are poor[38]. 11080, pp. On the one hand, predictions must have high accuracy, as, for instance, false negative predictions mean that necessary adaptations are missed. Advances in Neural Information Processing Systems, 2003. Related Work. 100, 129140 (2017), Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. [2] P. Izmailov, S. Vikram, M.D. Ensemble PyTorch is a unified ensemble framework for PyTorch to easily improve the performance and robustness of your deep learning model. This perspective provides several conceptually important and actionable takeaways, and prevents a problematic and arbitrary division of the literature. In particular, we will consider different shapes of penalties and different costs of adaptations. In [2], Hamiltonian Monte Carlo procedures are distributed over hundreds of TPUs, to investigate the fundamental properties of neural network posteriors. We use four data sets from different sources. Ensemble Deep Learning | Ensemble Deep Learning Models Proactive Process Adaptation Using Deep Learning Ensembles. OCT images before and after performing data augmentation. As an example, RNN ensembles provide an 8.4% higher accuracy when compared with a single RNN model (as used in[20]). Typically, prediction accuracy increases as the process unfolds, as more information about the process instance becomes available. Thereby, we avoid the problem RNNs may have in predicting the next process activity when process execution entails loops with many repeated activities[9, 34]. Now lets step back and understand what we are trying to compute from a Bayesian perspective. \end{equation*}, \begin{equation*} In deep learning, the integral we are trying to compute is over a multi-million dimensional parameter space, and the posterior is highly non-Gaussian and multi-modal. If during the execution of such freight transport process a delay is predicted, faster transport services (such as air delivery instead of road delivery) can be proactively scheduled in order to prevent the delay. Join 31K+ AI People keeping in touch with the most important ideas in Machine Learning through my free newsletter over here, Recently I came across an interesting Paper named, Deep Ensembles: A Loss Landscape Perspective by a Laxshminarayan et al.In this article, I will break down the paper, summarise its findings and delve into some of the techniques and strategies they used that will be useful for delving into understanding models and their, Deep Insights about Artificial Intelligence (AI), Machine Learning, Software Engineering, and the Tech Industry. [4] A.G. Wilson. case) will unfold upto its completion[8, 16, 22]. We consider the following independent variables (also shown in Table1): Reliability threshold \(\theta \in [.5,1]\). A comprehensive review on ensemble deep learning: Opportunities and Since deep ensembles perform approximate inference, there are many ways in which deep ensembles do not perfectly live up to a Bayesian ideal, as with any Bayesian inference procedure in deep learning. Although deep ensembles represent a relatively high fidelity approximation to the Bayesian posterior predictive distribution in practice, it is perfectly reasonable to further improve the approximation. Moreover, the view of forming the Bayesian predictive distribution as numerical integration rather than obtaining samples for simple Monte Carlo estimation opens the doors to many fresh approaches to approximate inference, lending itself naturally to an active learning perspective. In addition we compute reliability estimates for each prediction by computing the fraction of prediction models that predicted the majority class[19]. On the other hand, predictions should be produced early during process execution, as this leaves more time for adaptations, which typically have non-negligible latencies. The figure shows the costs of the dynamic approach (bold) and the costs of the static approach for each of the possible checkpoints (dashed). (c): Predictive distribution for factorized variational inference (VI). training data used and algorithm/model architecture. : Outcome-oriented predictive process monitoring: review and benchmark. p(y|x,\mathcal{D}) = \int p(y|x,w) p(w|\mathcal{D}) dw, Also independent of the static or dynamic approach it can be observed that if adaptation costs get higher, a higher threshold (and thus more conservative stance in taking adaptation decisions) offers cost savings. Mag. The cost savings for higher thresholds can become smaller than the cost savings for lower thresholds. Non-Bayesian deep ensembles typically outperformed these Bayesian methods, both in accuracy and calibration. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. In Figure 2, we consider a simple regression problem from [1], with three different approximations to the Bayesian predictive distribution: an HMC reference, a variational approximation with a Gaussian approximate posterior, and deep ensembles. Electronic Research Archive, 2023, 31(8): 4843-4861. doi: 10.3934/era.2023248. Proactive adaptation decisions are taken on a case by case basis. Ensemble deep learning: A review To improve aggregate prediction accuracy, deep learning techniques are being employed for predictive process monitoring[5, 9, 17, 26, 27, 34]. The popular Gaussian priors give rise to L2 regularization. In general, although deep ensembles provide a high-fidelity approximation to the Bayesian predictive distribution relative to standard approaches, there are also many obvious steps one can take to move deep ensembles closer to a fully Bayesian approach. 547562Cite as, Part of the Lecture Notes in Computer Science book series (LNISA,volume 11483). (s), (t), (u), (v), and (w) illustrate saturation changes. Deep learning ensemble models based on CNNs were proposed in [4, 7]. Google Scholar, Gutirrez, A.M., Cassales Marquezan, C., Resinas, M., Metzger, A., Ruiz-Corts, A., Pohl, K.: Extending WS-agreement to support automated conformity check on transport and logistics service agreements. As a follow up, Francescomarino et al. Springer, Cham (2016). Inf. This is an open access article distributed Moreover, for computational reasons, we are typically limited to about 10-100 points in the parameter space that we can evaluate to form our approximation to the posterior. The Case for Bayesian Deep Learning. Further performance speedups are possible via special-purpose hardware and RNN implementations. LNCS, vol. We use deep learning ensembles to produce predictions at arbitrary points during process execution. [7] M. Osborne. As an example, a delay in the expected delivery time for a freight transport process may incur contractual penalties[11]. The "Select an Image" button allows the user to browse to the location of a stored image and upload it to the webservice, and the "Predict" button sends the image to a deep learning server and receives the diagnosis class, Figure6. When determining checkpoints, there is an important tradeoff to be taken into account between prediction accuracy and the earliness of the prediction[13]. If we generally are worried about covering mass within a mode, then we can simply resolve this issue with MultiSWAG [1], which uses a mixture of Gaussians approximation to the posterior, while retaining the advantages of deep ensembles. Adapt. The top panels show the Wasserstein distance between the true predictive distribution and the deep ensemble and VI approximations, as a function of inputs \(x\). 10253, pp. Response: Part of this objection likely arises by viewing Bayesian model averaging purely through the prism of simple Monte Carlo integration. This paradigm . A similar tradeoff between accuracy and earliness was investigated for time series classification, i.e., for predicting the class label of a temporally-indexed set of data points. Moreover, even if we could use full batch MCMC, we never get anywhere near the asymptotic regime, and typically just use a handful of samples in practice. In addition to designing individual deep learning models, researchers have also explored the ensemble technique 27 in search of more robust and superior performance 25,28,29,30. To the best of our knowledge, this is the rst comprehensive review paper on deep ensemble models. By definition, maximum a-posteriori optimization involves a prior. To address these difficulties, we employ the following two solutions (presented in earlier work[20]). Proactive adaptation decisions are based on predictions about how an ongoing process instance will unfold upto its completion. A candidate sub . Deep learning ensembles for melanoma recognition in dermoscopy images We performed a secondary registration based on the original dataset. Aschoff, R., Zisman, A.: QoS-driven proactive adaptation of service composition. deep_ensemble_learning. Journal of Medical Internet Research - Machine and Deep Learning for Deep learning ensemble 2D CNN approach towards the detection of lung In: La Rosa, M., Loos, P., Pastor, O. and meta-analysis of 10 included studies that provided confusion matrix results. Generating the ensembles using bagging also contributes to the scalability of our approach, as the training of the base learners can happen in parallel. (8) Claim: When our posterior is unimodal, deep ensembles are a silly posterior approximation. If anything, the take home message should be the exact opposite! Adaptation effectiveness \(\alpha \in (0,1]\). This means they evaluate their proposed predictive process monitoring techniques by considering prediction earliness in addition to prediction accuracy. To be able to concisely analyze and present our experimental results, we assume constant costs and penalties (like we did in[19]). Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. However Such models require a large amount of labeled data (think millions of annotated images) to perform optimally. For diagnostic purpose, ECG . Comput. However, even via the simple MC perspective, we would expect deep ensemble models to receive close to an equal weighting: the models represent different SGD solutions, which will have very similar values of the loss, and the posterior will have very similar geometry around these solutions. Later predictions typically have a higher accuracy, because more information about the ongoing process instance is available. As added benefit, predictions computed via such ensembles provide higher prediction accuracy than using a single RNN model. Figure 2. We compute predictions and reliability estimates from ensembles of deep learning models, specifically RNN models. Video. Because deep ensembles represent approximate inference, there are valid ways in which they will differ from the Bayesian ideal. Retinal diseases classification based on hybrid ensemble deep learning If a potential problem is predicted, adaptation decisions are taken at run time to prevent or mitigate the predicted problem. Figure1 provides an overview of the main activities of our approach. In: Giorgini, P., Weber, B. The way the literature is being divided into Bayesian and non-Bayesian approaches is arbitrary and problematic. (eds.) Ensemble deep learning, which combines multiple deep neural networks, has emerged as a powerful approach for improving the generalization of learning systems. The ensemble of RNN models creates a prediction \(T_j\) at each potential checkpoint j. To cover different situations that may be faced in practice, we specifically chose different reliability thresholds, different probabilities of effective process adaptations, as well as different slopes for how these probabilities diminish towards the end of process execution. 235 Citations 13 Altmetric Metrics Abstract Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). PubMedGoogle Scholar. For the sake of generalizability, we used a nave approach to select data from the event log, i.e., we used whatever data is available and did not perform any manual feature engineering or selection. 10601, pp. If we want to avoid information loss, other prediction techniques would require the training of c prediction models, one for each of the c checkpoints. BPM 2018. First, instead of incrementally predicting the next process event until we reach the final event and thus the process outcome (such as proposed in[5, 9, 34]), we directly predict the process outcome. Distrib. Why not just have a workshop about deep ensembles?, was a common question at the annual Bayesian deep learning workshop, at NeurIPS 2019. : Ensemble Methods: Foundations and Algorithms. We use RNNs with Long Short-Term Memory (LSTM) cells as they better capture long-term dependencies in the data[17, 34]. If the reliability for a given prediction is equal to or greater than a predefined threshold, the prediction is used to trigger a proactive adaptation. BPM 2017. Problem. The key idea of our approach is to (i) dynamically determine, for each process instance, the earliest checkpoint that delivers a sufficiently high reliability, and (ii) use this checkpoint to decide on proactive adaptation. investigate in how far hyper-parameter optimization[7] and clustering[6] can improve earliness. Knowing the accuracy of an individual prediction is important, because some predictions may have a higher probability of being correct than others. (1) Claim: Deep ensembles do model combination instead of Bayesian model averaging. This means that the probability for effective proactive adaptations diminishes towards the end of the process. \end{aligned}$$, $$\begin{aligned} \rho _j = {max_{i = 1, \ldots , m}(\frac{|i : T_{i,j} = \text {``violation''}|}{m}, \frac{|i : T_{i,j} = \text {``non-violation''}|}{m})} \end{aligned}$$, https://doi.org/10.1007/978-3-030-21290-2_34, https://github.com/Chemsorly/BusinessProcessOutcomePrediction, https://doi.org/10.1007/978-3-642-25535-9_28, https://doi.org/10.1007/978-3-319-39696-5_22, https://doi.org/10.1007/978-3-319-98648-7_27, https://doi.org/10.1007/978-3-319-65000-5_15, https://doi.org/10.1007/978-3-642-45005-1_47, https://doi.org/10.1007/978-3-319-23063-4_21, https://doi.org/10.1007/978-3-319-07881-6_31, https://doi.org/10.1007/978-3-319-69035-3_25, https://doi.org/10.1007/978-3-319-59536-8_28, https://doi.org/10.1007/978-3-642-36249-1_9, https://doi.org/10.1007/978-3-319-98648-7_16, https://doi.org/10.1007/978-3-319-98648-7_29, https://doi.org/10.1007/978-3-319-59536-8_30, https://doi.org/10.1007/978-3-319-45348-4_23. Several authors use prediction earliness as a dependent variable in their experiments. Huiyi Hu *, As part of our future work, we will extend our dynamic approach towards non-constant cost models. In particular this means, that there is no need for a testing phase during which aggregate accuracies are computed in order to select a suitable static checkpoint. Indeed if we could work with the exact posterior then we wouldnt need variational inference. The majority of these attempts revolve around creating a weighted average of deep learning models. https://doi.org/10.1007/978-3-319-59536-8_28, Metzger, A., Neubauer, A.: Considering non-sequential control flows for process prediction with recurrent neural networks. There is more to be gained by exploring new basins, than continuing to explore the same basin. Response: This claim is somewhat ironic because the success of deep ensembles is largely because they do provide a relatively diverse collection of models compared to the models we would obtain, for example, by sampling from a unimodal MFVI posterior. Thus, the costs of a process adaptation, \(c_a\), are expressed as a fraction of the penalty for process violation, \(c_p\), i.e., \(c_a = \lambda \cdot c_p\). (eds.) To reflect the fact that earlier checkpoints may be favored as they provide more options and time for proactive adaptations (see Sect. Moreover, in deep learning, we are not aware of any approximate inference procedure that would have good approximation guarantees, except perhaps a narrow range of MCMC methods (typically full batch) run for an asymptotically long period of time. In contrast, other prediction techniques (such as random forests or multi-layer perceptrons) either require training a prediction model for each of the checkpoints or they require the special encoding of the input data to train a single model[16, 22, 36]. Download PDF Abstract: Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. Column A shows the number of situations for which proactive adaptation leads to lower costs than the baseline costs when not performing an adaptation. An effective ensemble deep learning framework for text classification

Hoffman Estates High School Website, How To Decline Someone Who Wants To Hang Out, Articles D