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1. A Generative Reinforcement Learning Framework for Predictive Analytics

   Skordilis, Erotokritos; Moghaddass, Ramin.; Farhat, Tazin. (January 2023, Proceedings annual Reliability and Maintainability Symposium)

   In this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.To our knowledge, the method presented in this paper is the first in industrial prognostics that utilizes generative models and reinforcement learning in that capacity. While the process requires extensive data preprocessing and environment tailored design, which is not always possible, it demonstrates the ability of generative models working in conjunction with reinforcement learning to provide proper value estimations for system dynamics and their RUL. To validate the quality of the proposed method, we conducted numerical experiments using the train_FD002 dataset provided by the NASA CMAPSS data repository. Different subsets were used to train the VAE and the RL agent, and a leftover set was then used for model validation. The results shown prove the merit of our method and will further assist us in developing a data-driven RL environment that incorporates more complex latent dynamic layers, such as normal/faulty operating conditions and hazard processes. 

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2. Learning multi-layer latent variable model with short run MCMC inference dynamics

   Nijkamp, E.; Pang, B.; Han, T.; Zhou, L.; Zhu, S. C.; Wu, Y. N. (January 2021, European Conference on Computer Vision)

   This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use noise initialized non-persistent short run MCMC, such as nite step Langevin dynamics initialized from the prior distribution of the latent variables, as an approximate inference engine, where the step size of the Langevin dynamics is variationally optimized by minimizing the Kullback-Leibler divergence between the distribution produced by the short run MCMC and the posterior distribution. Our experiments show that the proposed method outperforms variational auto-encoder (VAE) in terms of reconstruction error and synthesis quality. The advantage of the proposed method is that it is simple and automatic without the need to design an inference model. 

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3. Guided Variational Autoencoder for Disentanglement Learning

   Ding, Zheng; Xu, Yifan; Xu, Weijian; Parmar, Gaurav; Yang, Yang Yang; Welling, Max; Tu, Zhuowen (June 2020, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing signals to the latent encoding/embedding in VAE without changing its main backbone architecture, hence retaining the desirable properties of the VAE. We design an unsupervised strategy and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE. In the unsupervised strategy, we guide the VAE learning by introducing a lightweight decoder that learns latent geometric transformation and principal components; in the supervised strategy, we use an adversarial excitation and inhibition mechanism to encourage the disentanglement of the latent variables. Guided-VAE enjoys its transparency and simplicity for the general representation learning task, as well as disentanglement learning. On a number of experiments for representation learning, improved synthesis/sampling, better disentanglement for classification, and reduced classification errors in meta learning have been observed. 

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4. Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

   Yang, Wanqian; Kirichenko, Polina; Goldblum, Micah; Wilson, Andrew Gordon (September 2022, NeurIPS)

   Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. In addition to demonstrating the efficacy of Chroma-VAE on benchmark and real-world shortcut learning tasks, our work highlights the potential for manipulating the latent space of generative classifiers to isolate or interpret specific correlations. 

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5. Analytical Probability Distributions and EM-Learning for Deep Generative Networks

   Balestriero, Randall; Paris, Sebastien; Baraniuk, Richard G. (January 2020, Advances in neural information processing systems)

   Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs). In the absence of a known analytical form for the posterior and likelihood expectation, VAEs resort to approximations, including (Amortized) Variational Inference (AVI) and Monte-Carlo (MC) sampling. We exploit the Continuous Piecewise Affine (CPA) property of modern DGNs to derive their posterior and marginal distributions as well as the latter's first moments. These findings enable us to derive an analytical Expectation-Maximization (EM) algorithm that enables gradient-free DGN learning. We demonstrate empirically that EM training of DGNs produces greater likelihood than VAE training. Our findings will guide the design of new VAE AVI that better approximate the true posterior and open avenues to apply standard statistical tools for model comparison, anomaly detection, and missing data imputation. 

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