Results 61 to 70 of about 4,772,239 (326)
Quasi-Symplectic Langevin Variational Autoencoder [PDF]
, 2021 Variational autoencoder (VAE) is a very popular and well-investigated generative model in neural learning research. To leverage VAE in practical tasks dealing with a massive dataset of large dimensions, it is required to deal with the difficulty of ...
Wang, Zihao, Delingette, Hervé
core +3 more sources
Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis
Mathematics, 2023 This paper presents the functional subspace variational autoencoder, a technique addressing challenges in sensor data analysis in transportation systems, notably the misalignment of time series data and a lack of labeled data.
Tan Li +4 more
doaj +1 more source
Fully Spiking Variational Autoencoder
, 2022 Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature.
Mukuta, Yusuke +2 more
core +1 more source
Scientific Reports, 2021 Chromatin conformation plays an important role in a variety of genomic processes. Hi-C is one of the most popular assays for inspecting chromatin conformation. However, the utility of Hi-C contact maps is bottlenecked by resolution.
Max Highsmith, Jianlin Cheng
doaj +1 more source
NeuroImage, 2023 The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales.
Gerard Martí-Juan +3 more
semanticscholar +1 more source
Advances in Neural Information Processing Systems 36, 2023 Advances in Neural Information Processing Systems ...
Laura Manduchi +3 more
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Learning to balance the coherence and diversity of response generation in generation-based chatbots
International Journal of Advanced Robotic Systems, 2020 Generating response with both coherence and diversity is a challenging task in generation-based chatbots. It is more difficult to improve the coherence and diversity of dialog generation at the same time in the response generation model. In this article,
Shuliang Wang +4 more
doaj +1 more source
Bayesian Structural Model Updating with Multimodal Variational Autoencoder [PDF]
Computer Methods in Applied Mechanics and EngineeringA novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE).
Tatsuya Itoi +3 more
semanticscholar +1 more source
AAAI Conference on Artificial Intelligence, 2022 As an asset pricing model in economics and finance, factor model has been widely used in quantitative investment. Towards building more effective factor models, recent years have witnessed the paradigm shift from linear models to more flexible nonlinear ...
Yitong Duan +3 more
semanticscholar +1 more source
Ladder Variational Autoencoders
, 2016 Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models.
Sønderby, Casper Kaae +4 more
openaire +4 more sources