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Variational Inference

1946 directly classified papers

Papers per year

Papers

Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization AAAI 2021
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors AAAI 2021
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction AAAI 2021
UWSpeech: Speech to Speech Translation for Unwritten Languages AAAI 2021
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations AAAI 2021
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization AAAI 2021
Content Learning with Structure-Aware Writing: A Graph-Infused Dual Conditional Variational Autoencoder for Automatic Storytelling AAAI 2021
Spherical Image Generation from a Single Image by Considering Scene Symmetry AAAI 2021
Open-Set Recognition with Gaussian Mixture Variational Autoencoders AAAI 2021
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs AAAI 2021
Physarum Powered Differentiable Linear Programming Layers and Applications AAAI 2021
Multi-stream based marked point process ACML 2021
Generalized Zero-Shot Learning via Disentangled Representation AAAI 2021
Cross-structural Factor-topic Model: Document Analysis with Sophisticated Covariates ACML 2021
DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks ACML 2021
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty AAAI 2021
Improving Gaussian mixture latent variable model convergence with Optimal Transport ACML 2021
Event Representation with Sequential, Semi-Supervised Discrete Variables NAACL 2021
Capturing Uncertainty in Unsupervised GPS Trajectory Segmentation Using Bayesian Deep Learning AAAI 2021
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting AAAI 2021
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders AAAI 2021
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency NIPS 2021
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding ICML 2021
Rectangular Flows for Manifold Learning NIPS 2021
A Differentiable Point Process with Its Application to Spiking Neural Networks ICML 2021