← Models
Deep Learning › Models ›

Variational Inference

1946 directly classified papers

Papers per year

Papers

Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing NIPS 2023
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods NIPS 2023
Robust Weak Supervision with Variational Auto-Encoders ICML 2023
A Targeted Accuracy Diagnostic for Variational Approximations AISTATS 2023
Pluralistic Aging Diffusion Autoencoder ICCV 2023
Coupled Variational Autoencoder ICML 2023
Disentanglement via Latent Quantization NIPS 2023
Differentiable Random Partition Models NIPS 2023
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow NIPS 2023
Compression with Bayesian Implicit Neural Representations NIPS 2023
Learn to Accumulate Evidence from All Training Samples: Theory and Practice ICML 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference ICML 2023
Creative Painting with Latent Diffusion Models COLING 2022
Variational- and metric-based deep latent space for out-of-distribution detection UAI 2022
Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently UAI 2022
Predictive Coding beyond Gaussian Distributions NIPS 2022
Variational Stacked Local Attention Networks for Diverse Video Captioning WACV 2022
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis NIPS 2022
On the detrimental effect of invariances in the likelihood for variational inference NIPS 2022
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022
Structured Recognition for Generative Models with Explaining Away NIPS 2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels NIPS 2022
Decoupled Self-supervised Learning for Graphs NIPS 2022
Foundation Posteriors for Approximate Probabilistic Inference NIPS 2022
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression CVPR 2022