Results 41 to 50 of about 13,735 (297)
A Quaternion-Valued Variational Autoencoder [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Accepted for publication at the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Grassucci E., Comminiello D., Uncini A.
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Three Variations on Variational Autoencoders
CoRR, 2022 21 pages. This version, v2, has added an explicit evaluation of our VAE A variational encoder.
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Overdispersed variational autoencoders [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 The ability to fit complex generative probabilistic models to data is a key challenge in AI. Currently, variational methods are popular, but remain difficult to train due to high variance of the sampling methods employed. We introduce the overdispersed variational autoencoder and overdispersed importance weighted autoencoder, which combine ...
Harshil Shah +2 more
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Disentangling Variational Autoencoders
CoRR, 2022 A variational autoencoder (VAE) is a probabilistic machine learning framework for posterior inference that projects an input set of high-dimensional data to a lower-dimensional, latent space. The latent space learned with a VAE offers exciting opportunities to develop new data-driven design processes in creative disciplines, in particular, to automate ...
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Advances in Neural Information Processing Systems 36, 2023 Advances in Neural Information Processing Systems ...
Laura Manduchi +3 more
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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
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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
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Variational Laplace Autoencoders
CoRR, 2022 Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. However, such amortized variational inference faces two challenges: (1) the limited posterior expressiveness of fully-factorized Gaussian assumption and (2) the amortization error of the inference model.
Yookoon S. Park +2 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
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Variational autoencoder and latent space observation tasks [PDF]
, 2023 Variational autoencoder is an innovation in the field of unsupervised machine learning. Its architecture combines stochastic encoder-decoder modules and deep learning.
Faltejsek, Tomáš
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