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Variational Inference

1946 directly classified papers

Papers per year

Papers

Learning from Irregularly-Sampled Time Series: A Missing Data Perspective ICML 2020
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ACL 2020
Neural Mixed Counting Models for Dispersed Topic Discovery ACL 2020
Introducing Routing Uncertainty in Capsule Networks NIPS 2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective NIPS 2020
Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation AACL 2020
Weakly-Supervised Action Localization by Generative Attention Modeling CVPR 2020
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation CVPR 2020
Effective Estimation of Deep Generative Language Models ACL 2020
Semi-supervised Parsing with a Variational Autoencoding Parser ACL 2020
Amortized Population Gibbs Samplers with Neural Sufficient Statistics ICML 2020
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020
Learning to Learn Kernels with Variational Random Features ICML 2020
Perceptual Generative Autoencoders ICML 2020
Training Deep Energy-Based Models with f-Divergence Minimization ICML 2020
Variational Bayesian Quantization ICML 2020
Variational Label Enhancement ICML 2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables ICML 2020
Deep Gaussian Markov Random Fields ICML 2020
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation ICML 2020
ControlVAE: Controllable Variational Autoencoder ICML 2020
Learning Robot Skills with Temporal Variational Inference ICML 2020
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation CVPR 2020
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders CVPR 2020
A Batch Normalized Inference Network Keeps the KL Vanishing Away ACL 2020