Suchergebnisse - Hamiltonian Variational Autoencoder

Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes von Kebaili, Aghiles, Lapuyade-Lahorgue, Jérôme, Vera, Pierre, Ruan, Su

“… with limited training datasets. To this end, we propose a new end-to-end hybrid architecture based on Hamiltonian Variational Autoencoders (HVAE …”
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Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization von Kwon, Hee Young, Yoon, Han Gyu, Park, Sung Min, Lee, Doo Bong, Choi, Jun Woo, Won, Changyeon

“… A variational autoencoder model is devised and applied to a magnetic system to generate energetically stable magnetic states with low local deformation …”
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Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes von Aghiles Kebaili, Lapuyade-Lahorgue, Jérôme, Vera, Pierre, Ruan, Su

“… with limited training datasets. To this end, we propose a new end-to-end hybrid architecture based on Hamiltonian Variational Autoencoders (HVAE …”
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Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders von Wetzel, Sebastian J

“… The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders …”
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Quasi-symplectic Langevin Variational Autoencoder von Wang, Zihao, Delingette, Hervé

“… ). Markov Chain Monte Carlo (MCMC) is an effective approach to tighten the ELBO for approximating the posterior distribution and Hamiltonian Variational Autoencoder (HVAE …”
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Variational Autoencoder Reconstruction of Complex Many-Body Physics von Luchnikov, Ilia A., Ryzhov, Alexander, Stas, Pieter-Jan, Filippov, Sergey N., Ouerdane, Henni

“… Thermodynamics is a theory of principles that permits a basic description of the macroscopic properties of a rich variety of complex systems from traditional …”
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Learning variational autoencoders via MCMC speed measures von Hirt, Marcel, Kreouzis, Vasileios, Dellaportas, Petros

“… Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximising an evidence lower bound …”
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End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks von Aghiles Kebaili, Lapuyade-Lahorgue, Jérôme, Vera, Pierre, Ruan, Su

“… To address these limitations, we present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE …”
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Asynchronous multimodal PINN pre-train framework based on TransVNet(MPP-TV) and its application in numerical solutions of the Cauchy problem for the Hamilton-Jacobi equation von Chen, Tianhao, Li, Zeyu, Xu, Pengbo, Zheng, Haibiao

“… through TransVNet, which is a novel autoencoder architecture proposed in this paper, we seamlessly integrate Hamiltonian …”
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Variational autoencoder reconstruction of complex many-body physics von Luchnikov, I, Ryzhov, A, Stas, P -J C, Filippov, S N, Ouerdane, H

“… In the present work, we show how to use a variational autoencoder (VAE) -- a state-of-the-art tool in the field of deep learning for the simulation …”
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Physics-informed neural networks via stochastic Hamiltonian dynamics learning von Bajaj, Chandrajit, Nguyen, Minh

“… This is achieved through utilizing a variational autoencoder which leads to more effective path exploration process. We apply our learning frameworks called NeuralPMP to various control tasks and obtain competitive results …”
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Neural network flows of low q-state Potts and clock models von Giataganas, Dimitrios, Huang, Ching-Yu, Lin, Feng-Li

“… Here we construct a variety of neural network (NN) flows using the RBM and (variational) autoencoders, to study the q -state Potts and clock models on the square lattice for q …”
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Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders von Wetzel, Sebastian Johann

“… These methods range from principal component analysis to artificial neural network based variational autoencoders …”
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Learning the Shape of Evolutionary Landscapes: Geometric Deep Learning Reveals Hidden Structure in Phenotype-to-Fitness Maps von Razo-Mejia, Manuel, Mani, Madhav, Petrov, Dmitri A.

“… in a geometry-informed manner. We demonstrate that our approach using a Riemannian Hamiltonian Variational Autoencoder (RHVAE …”
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Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding von Wu, Ga, Domke, Justin, Sanner, Scott

“… Variational Autoencoders (VAEs) are a popular generative model, but one in which conditional inference can be challenging …”
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Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders von Andrés-Terré, Helena, Lió, Pietro

“… The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years …”
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Symplectic encoders for physics-constrained variational dynamics inference von Bacsa, Kiran, Lai, Zhilu, Liu, Wei, Todd, Michael, Chatzi, Eleni

“… We propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom (MDOF) dynamic systems …”
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Latent generative landscapes as maps of functional diversity in protein sequence space von Ziegler, Cheyenne, Martin, Jonathan, Sinner, Claude, Morcos, Faruck

“… Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve …”
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Model Selection for Bayesian Autoencoders von Ba-Hien Tran, Rossi, Simone, Milios, Dimitrios, Michiardi, Pietro, Bonilla, Edwin V, Filippone, Maurizio

“… We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs …”
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Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes von Ba-Hien Tran, Shahbaba, Babak, Mandt, Stephan, Filippone, Maurizio

“… Autoencoders and their variants are among the most widely used models in representation learning and generative modeling …”
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