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Continuous Latent Process Flows [article]

Ruizhi Deng, Marcus A. Brubaker, Greg Mori, Andreas M. Lehrmann
2021 arXiv   pre-print
We tackle these challenges with continuous latent process flows (CLPF), a principled architecture decoding continuous latent processes into continuous observable processes using a time-dependent normalizing  ...  flow driven by a stochastic differential equation.  ...  Continuous Latent Process Flows A Continuous Latent Process Flow consists of two major components: an SDE describing the continuous latent dynamics of an observable stochastic process and a continuously  ... 
arXiv:2106.15580v2 fatcat:p6b5f6wghjf7laqk4sjlhzg4xy

Latent Variables-Based Process Modeling of a Continuous Hydrogenation Reaction in API Synthesis of Small Molecules

Zhenqi Shi, Nikolay Zaborenko, David E. Reed
2013 Journal of Pharmaceutical Innovation  
Conclusions The capabilities of latent variables-based process modeling have been well demonstrated as applied to a continuous-flow hydrogenation reaction, regarding its improved process understanding  ...  Methods The case presented here is the first application of latent variables-based modeling to a reaction process in smallmolecule active pharmaceutical ingredient route synthesis, i.e., a continuous-flow  ...  Performing hydrogenations in a continuous-flow tubular reactor allows for extended processing without the need to depressurize, to empty and clean the reactor, or to refill it  ... 
doi:10.1007/s12247-012-9141-y fatcat:tuskgwaaofhetfda3b4bgy25ny

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows [article]

Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann
2021 arXiv   pre-print
In this work, we propose a novel type of normalizing flow driven by a differential deformation of the Wiener process.  ...  Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation.  ...  Figure 2 : 2 (Latent) Continuous-Time Flow Processes (CTFPs). (a) Likelihood calculation.  ... 
arXiv:2002.10516v4 fatcat:75xbi3uiuff35mp2tlqguriy6q

Latent Normalizing Flows for Discrete Sequences [article]

Zachary M. Ziegler, Alexander M. Rush
2019 arXiv   pre-print
Normalizing flows are a powerful class of generative models for continuous random variables, showing both strong model flexibility and the potential for non-autoregressive generation.  ...  We propose a VAE-based generative model which jointly learns a normalizing flow-based distribution in the latent space and a stochastic mapping to an observed discrete space.  ...  We begin by describing the full generative process and then focus on the flow-based prior. Generating Discrete Sequences Our central process will be a latent-variable model for a discrete sequence.  ... 
arXiv:1901.10548v4 fatcat:7g3dbgmck5hbfoitsguucn2yse

3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction [article]

Shuaihang Yuan, Xiang Li, Anthony Tzes, Yi Fang
2020 arXiv   pre-print
Moreover, a temporally consistent motion Morpher is proposed to learn a continuous flow field which deforms a 3D scan from the current frame to the next frame.  ...  To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent  ...  A learnable latent code is designed Fig. 2 . The pipeline of the proposed method. We proposed two models to learn a continuous flow function that can predict the future motion.  ... 
arXiv:2006.13906v1 fatcat:mrguq5nbebhanl3dh25clgma6y

The U-Net based GLOW for Optical-Flow-free Video Interframe Generation [article]

Saem Park, Donghoon Han, Nojun Kwak
2021 arXiv   pre-print
In addition, we propose a learning method with a new consistency loss in the latent space to maintain semantic temporal consistency between frames.  ...  So, instead of simply averaging two adjacent frames to create an intermediate image, this operation should maintain semantic continuity with the adjacent frames.  ...  In general DNN, due to the non-linear function and pooling process, it is impossible to perform reverse processing from the latent space to the original shape again.  ... 
arXiv:2103.09576v3 fatcat:sau35rbumbhp5cyngjjw34e2lu

RBM-Flow and D-Flow: Invertible Flows with Discrete Energy Base Spaces [article]

Daniel O'Connor, Walter Vinci
2021 arXiv   pre-print
Furthermore, we also obtain D-Flow, an IF model with uncorrelated discrete latent variables.  ...  In this paper we seek to mitigate this by implementing RBM-Flow, an IF model whose base distribution is a Restricted Boltzmann Machine (RBM) with a continuous smoothing applied.  ...  Checkerboard split and squeeze processes refer to dimension reshaping processes used in FLOW++ to increase the number of dimensions in the image channel such that the flow can ensure the equal partitioning  ... 
arXiv:2012.13196v3 fatcat:4losmcinsfcb3kbsbw2autkruy

Shape optimization in laminar flow with a label-guided variational autoencoder [article]

Stephan Eismann, Stefan Bartzsch, Stefano Ermon
2017 arXiv   pre-print
Jointly training an architecture combining a variational autoencoder mapping shapes to latent representations and Gaussian process regression allows us to generate improved shapes in the two dimensional  ...  In this work, we explore a Bayesian optimization approach to minimize an object's drag coefficient in laminar flow based on predicting drag directly from the object shape.  ...  Natural extensions of this work include the application of the presented approach to the case of three dimensions and turbulent flow.  ... 
arXiv:1712.03599v1 fatcat:timb7zdr6fghfhf7iaxj5ntl7e

Discrete flow posteriors for variational inference in discrete dynamical systems [article]

Laurence Aitchison, Vincent Adam, Srinivas C. Turaga
2018 arXiv   pre-print
While it is possible to use normalizing flow approximate posteriors for continuous latents, some problems have discrete latents and strong statistical dependencies.  ...  latent variables and interpreting the K-step fixed-point iterations as a normalizing flow.  ...  First, we considered applying normalizing flows by transforming our discrete latents into continuous latents, which are thresholded to recover the original discrete variables [12] .  ... 
arXiv:1805.10958v1 fatcat:2mgiyebewvgyjav73j7fyvjw4y

Neural Jump Stochastic Differential Equations [article]

Junteng Jia, Austin R. Benson
2020 arXiv   pre-print
We then model temporal point processes with a piecewise-continuous latent trajectory, where the discontinuities are caused by stochastic events whose conditional intensity depends on the latent state.  ...  Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events.  ...  The Neural ODEs framework models continuous transformation of a latent vector as an ODE flow and parameterizes the flow dynamics with a neural network Chen et al. (2018) .  ... 
arXiv:1905.10403v3 fatcat:xsxnrlm6d5gz7c3jcb4ht4mchy

Solar energy based thermal energy storage system using phase change materials

R. Meenakshi Reddy, N. Nallusamy, T. Hariprasad, K. Hemachandra Reddy, G. Ramachandra Reddy
2012 International Journal of Renewable Energy Technology  
Open loop (Discharging) trials were carried out by both continuous and batch wise processes.  ...  The charging process was continued until the PCM temperature Tp attained maximum temperature.  ... 
doi:10.1504/ijret.2012.043905 fatcat:2khiu2lgmzgevne57ucqao3vti

Coupled Model of Heat and Mass Balance for Droplet Growth in Wet Steam Non-Equilibrium Homogeneous Condensation Flow

Xu Han, Zhonghe Han, Wei Zeng, Jiangbo Qian, Zhi Wang
2017 Energies  
In the continuous flow region, the heat and mass balance model coincides with the Gyarmathy model.  ...  Therefore, the heat and mass balance model can be used to accurately describe the growth process of water droplets in the arbitrary range of Knudsen numbers.  ...  process.  ... 
doi:10.3390/en10122033 fatcat:kf3lg2ndu5d5pp2pwgsexqtxxu

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows [article]

Aihua Mao, Zihui Du, Junhui Hou, Yaqi Duan, Yong-jin Liu, Ying He
2022 arXiv   pre-print
Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points  ...  in a latent space, where the ensemble weights are adaptively learned from local geometric context.  ...  A.3 Continuous Flow Block Our continuous model consists of L flow blocks. Each continuous flow block is basically an ODE-Net implemented in FFJORD [41] .  ... 
arXiv:2107.05893v3 fatcat:vjte7ttw55bh5klf4jf7re3ytq

GraphDF: A Discrete Flow Model for Molecular Graph Generation [article]

Youzhi Luo, Keqiang Yan, Shuiwang Ji
2021 arXiv   pre-print
In this work, we propose GraphDF, a novel discrete latent variable model for molecular graph generation based on normalizing flow methods.  ...  While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate modeling of discrete graph structures.  ...  Then continuous flow µdij capture autoregressive information from elements in SG models map such pseudo continuous data to latent vari- before ai and bij , respectively.  ... 
arXiv:2102.01189v2 fatcat:tuekqqy6ajawhifcmeqwywpcgq

Business Process Outcome Prediction Based on Deep Latent Factor Model

Ke Lu, Xinjian Fang, Xianwen Fang
2022 Electronics  
Business process outcome prediction plays an essential role in business process monitoring. It continuously analyzes completed process events to predict the executing cases' outcome.  ...  To address these issues, this paper proposes a Deep Latent Factor Model Predictor (DLFM Predictor) for uncovering the implicit factors affecting system operation and predicting the final results of continuous  ...  involves the processing strategies of control flow and data flow, we show the process in Algorithm 1 to make it more transparent.  ... 
doi:10.3390/electronics11091509 fatcat:67iizwm3ezhrnaky3tn3ytyd7e
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