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We propose to learn energybased model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the topdown networkofthegeneratormodel. BoththelatentspaceEBMandthetopdown network can be learned jointly by maximum likelihood, which involves shortrun MCMC sampling from both the prior and posterior distributions of the latent vector. Due to the low dimensionality of the latent space and the expressiveness of the topdown network, a simple EBM in latent space can capture regularities in the data effectively, and MCMC sampling in latent space is efficient and mixes well. We show that the learnedmore »Free, publiclyaccessible full text available July 1, 2022

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are nonstationary in either spatial or temporal domain. We show that an energybased spatialtemporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatialtemporal ConvNet that consists of multiple layers of spatialtemporal filters to capture spatialtemporal patterns of different scales. The model can be learned from the training video sequences by an “analysis by synthesis” learning algorithm thatmore »

We propose a generative model of unordered point sets, such as point clouds, in the form of an energybased model, where the energy function is parameterized by an input permutation invariant bottomup neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into an energy for the whole point cloud. We call our model the Generative PointNet because it can be derived from the discriminative PointNet. Our model can be trained by MCMC based maximum likelihood learning (as well as its variants), without the help of any assisting networks like thosemore »

How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors andmore »

This paper studies the unsupervised crossdomain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy based model and a latent variable model. The use of generative cooperative network enables maximum likelihood learning of the domain model by MCMC teaching, where the energybased model seeks to fit the data distribution of domain and distills its knowledge to the latent variable model via MCMC. Specifically, in the MCMC teaching process, the latent variable model parameterized by an encoderdecoder maps examples from the source domainmore »

This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in topdown 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 nonpersistent short run MCMC, such as nite step Langevin dynamics initialized from the prior distribution of the latent variables, asmore »

Free, publiclyaccessible full text available March 1, 2022

Free, publiclyaccessible full text available July 1, 2022

Many unicellular microorganisms including yeasts, fungi, microalgae, and to a lesser extent bacteria (Fig. 1), can produce intra cellular edible oils under normal and specialized conditions (Papanikolaou and Aggelis, 2011). Most of these organisms can accumulate microbial lipids, or singlecell oils (SCOs), to 20%–90% (w/w) of their dry cell biomass. For example, fungi in the genus Mortierella and Umbelopsis can accumulate lipids at concentrations that exceed 86% of their dry weight (Meng et al., 2009). Microbial lipids can be produced using lowpriced organic materials, including wastestreams from the food industry, as growth substrate. The fatty acid of microbial lipids ismore »